Arts and Social Sciences, Irving K. Barber Faculty of (Okanagan)
Psychology, Department of (Okanagan)
DSpace
UBCO
Kaseweter, Kimberley Anne
Doctor of Philosophy - PhD
University of British Columbia
Pain has been identified as the most common reason for seeking medical care in emergency departments and yet has been found to be left widely undertreated. The pervasiveness of these visits, combined with the undertreatment of pain, can have detrimental consequences for patients and providers. As such, there is a pressing need to identify factors associated with effective pain treatment. A factor consistently found to motivate helping behaviour is personal distress; however, the intricacies of this relationship remain unclear. Specifically, willingness to help patients in pain has been linked to both high and low levels of personal distress. This dissertation aimed to clarify the nature of this relationship and to explore the potential moderating influence of both individual (i.e., third-person pain perception, emotion regulation, and psychopathic traits) and situational (i.e., cost of helping) factors. Two studies were conducted in which undergraduate students and medical students completed a prosocial task where they were asked to indicate how much time they wished to spend helping patients in pain. Overall, personal distress did not emerge as a significant predictor of helping behaviour, and this relationship was not moderated by the personal cost of helping (i.e., type of remuneration). Support was found, however, for the moderating role of individual traits. Specifically, Studies 1 and 2 identified three distinct emotional processing profiles: Sensitive Responders, Indifferent Responders, and Insensitive Responders. Moreover, these profiles were found to be differently related to prosocial behaviour. Overall, findings suggest that individual differences in emotional processing may moderate the relationship between empathy and prosocial behaviour, potentially contributing to disparities in the treatment of pain. Additionally, these findings highlight the importance of considering these traits simultaneously, as opposed to examining these variables in isolation.
i DEFINING THE LINE: EXAMINING THE ROLE OF EMPATHY IN THE PROSOCIAL TREATMENT OF PAIN by Kimberley Anne Kaseweter B.Sc., University of Northern British Columbia, 2011 M.Sc., University of Northern British Columbia, 2015 A DISSERTATION SUBMITTED IN PARTIAL FULLFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2020 © Kimberley Anne Kaseweter, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: DEFINING THE LINE: EXAMINING THE ROLE OF EMPATHY IN THE PROSOCIAL TREATMENT OF PAIN submitted by Kimberley Anne Kaseweter in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Psychology Examining Committee: Dr. Paul G. Davies, Irving K. Barber School of Arts and Sciences Supervisor Dr. Maya Libben, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Cynthia Mathieson, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Sarah Dow-Fleisner, Faculty of Health and Social Development University Examiner Dr. Tavis Campbell, Department of Psychology, University of Calgary External Examiner iii Abstract Pain has been identified as the most common reason for seeking medical care in emergency departments and yet has been found to be left widely undertreated. The pervasiveness of these visits, combined with the undertreatment of pain, can have detrimental consequences for patients and providers. As such, there is a pressing need to identify factors associated with effective pain treatment. A factor consistently found to motivate helping behaviour is personal distress; however, the intricacies of this relationship remain unclear. Specifically, willingness to help patients in pain has been linked to both high and low levels of personal distress. This dissertation aimed to clarify the nature of this relationship and to explore the potential moderating influence of both individual (i.e., third-person pain perception, emotion regulation, and psychopathic traits) and situational (i.e., cost of helping) factors. Two studies were conducted in which undergraduate students and medical students completed a prosocial task where they were asked to indicate how much time they wished to spend helping patients in pain. Overall, personal distress did not emerge as a significant predictor of helping behaviour, and this relationship was not moderated by the personal cost of helping (i.e., type of remuneration). Support was found, however, for the moderating role of individual traits. Specifically, Studies 1 and 2 identified three distinct emotional processing profiles: Sensitive Responders, Indifferent Responders, and Insensitive Responders. Moreover, these profiles were found to be differently related to prosocial behaviour. Overall, findings suggest that individual differences in emotional processing may moderate the relationship between empathy and prosocial behaviour, potentially contributing to disparities in the treatment of pain. Additionally, these findings highlight the importance of considering these traits simultaneously, as opposed to examining these variables in isolation. iv Lay Summary When seeking medical care, it has been found that patients often receive inadequate treatment for pain relief â the effects of which can cause lasting harm to patients. The overarching aim of this dissertation was to explore how various factors (e.g., personality traits and context) can influence the treatment of patients in pain. Two studies were conducted in which undergraduate students and medical residents completed a prosocial task where they were asked to indicate how much time they wished to spend helping patients in pain. It was hypothesized that a relationship would be found between participantsâ willingness to help and their level of personal distress. This relationship was also expected to be moderated by four factors: (a) third-person pain perception; (b) emotion regulation; (c) psychopathic characteristics; and (d) personal cost of helping. Results were partially in line with these hypotheses. On its own, personal distress did not predict helping behaviour, and this relationship did not change as a result of the personal cost of helping. However, personal distress did appear to interact with third-person pain perception, emotion regulation, and psychopathic traits, influencing the amount of help offered to patients in pain. v Preface This dissertation is an original intellectual product of the author, Kimberley A. Kaseweter. The University of British Columbiaâs Okanagan Behavioural Research Ethics Board granted ethical approval for this research on March 19th, 2018 and September 19th, 2018. The ethics approval certificate numbers for this research are H18-00417 and H18-01814, respectively. As of the date of this submission, the results in this dissertation have not been published. vi Table of Contents Abstract ................................................................................................................................... iii Lay Summary ......................................................................................................................... iv Preface .......................................................................................................................................v Table of Contents ................................................................................................................... vi List of Figures ......................................................................................................................... ix Acknowledgements...................................................................................................................x Dedication ............................................................................................................................... xi CHAPTER 1 Introduction ......................................................................................................1 1.1 Potential Moderating Variables................................................................................................. 5 1.1.1 Pain Perception ...................................................................................................................... 5 1.1.2 Emotion Regulation ............................................................................................................... 7 1.1.3 Psychopathic Traits................................................................................................................ 8 1.1.4 Personal Cost of Helping ..................................................................................................... 10 1.2 Project Summary ...................................................................................................................... 13 CHAPTER 2 Study 1 .............................................................................................................18 2.1 Study 1 Overview ...................................................................................................................... 18 2.2 Participants ................................................................................................................................ 18 2.3 Materials .................................................................................................................................... 19 2.3.1 Assessment of Prosocial Behavior ...................................................................................... 19 2.3.2 Assessment of Third-Person Pain Perception ...................................................................... 20 2.3.3 Self-Report Measures .......................................................................................................... 22 2.4 Procedure ................................................................................................................................... 24 2.4.1 Prosocial Behaviour Task .................................................................................................... 25 2.4.2 Third-Person Pain Perception Task ..................................................................................... 27 2.4.3 Self-Report Measures .......................................................................................................... 28 2.5 Data Reduction .......................................................................................................................... 28 2.5.1 Prosocial Behaviour ............................................................................................................. 28 2.5.2 Third-Person Pain Perception .............................................................................................. 28 2.5.3 Self-Report Measures .......................................................................................................... 30 2.5.4 Analytic Strategy ................................................................................................................. 30 2.6 Results ........................................................................................................................................ 32 2.6.1 Descriptive Statistics ........................................................................................................... 32 2.6.2 Research Question 1: Empathy and Prosocial Behaviour ................................................... 35 2.6.3 Research Question 2: Emotional Response Profiles............................................................ 36 2.7 Summary .................................................................................................................................... 40 2.7.1 Prosocial Behaviour Task .................................................................................................... 41 2.7.2 Research Question 1: Empathy and Prosocial Behaviour ................................................... 42 2.7.3 Research Question 2: Emotional Response Profiles............................................................ 43 CHAPTER 3 Study 2 .............................................................................................................45 3.1 Overview of Study 2 .................................................................................................................. 45 3.2 Participants ................................................................................................................................ 45 3.3 Materials .................................................................................................................................... 46 3.3.1 Assessment of Prosocial Behavior ...................................................................................... 46 vii 3.3.2 Assessment of Third-Person Pain ........................................................................................ 47 3.3.3 Self-Report Measures .......................................................................................................... 47 3.4 Procedure ................................................................................................................................... 47 3.4.1 Prosocial Behaviour Task .................................................................................................... 48 3.5 Data Reduction .......................................................................................................................... 49 3.6 Analytic Strategy ....................................................................................................................... 49 3.7 Results ........................................................................................................................................ 50 3.7.1 Descriptive Statistics ........................................................................................................... 50 3.7.2 Research Question 3: Personal Cost of Helping .................................................................. 56 3.7.3 Research Question 4: Generalizability of Emotional Response Profiles ............................ 57 3.7.4 Research Question 5: Comparing Predictors ....................................................................... 61 3.8 Summary .................................................................................................................................... 62 3.8.1. Prosocial Behaviour Task ................................................................................................... 63 3.8.2 Research Question 3: Personal Cost of Helping .................................................................. 64 3.8.3 Research Question 4: Generalizability of Emotional Response Profiles ............................ 64 3.8.4 Research Question 5: Comparing Predictors ....................................................................... 65 CHAPTER 4 General Discussion .........................................................................................68 4.1 Empathy and Prosocial Behaviour .......................................................................................... 69 4.2 Emotional Response Profiles .................................................................................................... 72 4.3 Personal Cost of Helping .......................................................................................................... 74 4.4 Implications ............................................................................................................................... 76 4.5 Strengths, Limitations, and Future Directions....................................................................... 78 4.6 Conclusion ................................................................................................................................. 81 Appendices ..............................................................................................................................99 Appendix A: Demographics Questionnaire .................................................................................. 99 Appendix B: Interpersonal Reactivity Index (IRI) .................................................................... 100 Appendix C: Emotion Regulation Questionnaire (ERQ) .......................................................... 101 Appendix D: Emotion Regulation Questionnaire (ERQ) .......................................................... 102 viii List of Tables Table 1 Descriptive Information for Prosocial Behaviour Task ................................................... 34 Table 2 Descriptive Information for Predictor Variables and Correlation with Helping Behaviour ...................................................................................................................................... 35 Table 3 Bivariate Pearson Correlations Among Indicator Variables ........................................ 37 Table 4 Fit Indices for Latent Profiles Defined by Emotional Response Variables ............... 37 Table 5 Profile Means on Each Indicator Variable ......................................................................... 39 Table 6 ANOVA Comparison with External Variables Across Profiles ..................................... 40 Table 7 Descriptive Information for Help Ratings in the Prosocial Behaviour Task ............ 51 Table 8 Descriptive Information for Number of Patients Seen in the Prosocial Behaviour Task ................................................................................................................................................. 54 Table 9 Descriptive Information for Predictor Variables Compared Between Participant Groups ........................................................................................................................................... 55 Table 10 Pearsonâs Correlations Between Predictor Variables and Helping Behaviour .... 56 Table 11 Bivariate Pearson Correlations Among Indicator Variables...................................... 58 Table 12 Fit Indices for Latent Profiles Defined by Emotional Response Variables ............ 58 Table 13 Profile means on each indicator variable ......................................................................... 60 Table 14 ANOVA Comparison with External Variables Across Profiles .................................. 61 Table 15 Factorial ANOVA Results for Number of Patients Seen............................................... 62 ix List of Figures Figure 1 Schematic Representation of Prosocial Behaviour Task .........................................27 Figure 2 Profile Means Across Indicator Variables ..............................................................38 Figure 3 Frequency Distribution of Helping Behaviour in Prosocial Behaviour Task .........53 Figure 4 Profile Means Across Indicator Variables ..............................................................59 x Acknowledgements This dissertation would not have been possible without the help and support of many people. First, I am forever grateful to my supervisor, Dr. Paul Davies, who not only provided me the mentorship and support necessary to complete this dissertation, but also reignited my passion for research and restored my faith in academia. None of this would have been possible without you â thank you! I would also like to extend my gratitude to Dr. Maya Libben, Dr. Cynthia Mathieson, Dr. Susan Holtzman, and Dr. Harry Miller. Your encouragement, knowledge, guidance, and genuine interest throughout this process has provided me with an environment to prosper. Thank you to Dr. Ken Prkachin, Dr. Philip Jackson, and Dr. Michel-Pierre Coll for providing me with the stimuli and materials used in the experimental tasks. In particular, I would like to acknowledge the continued mentorship Dr. Prkachin has provided throughout my studies. Thank you for being a wonderful academic role model. This dissertation would also not have been possible without the exceptional research assistance of students Kasia Niewinski, Victoria Scotney, Sara Stanton, and Rita Wakelin. Thank you so much for your hard-work and time. It was a pleasure to work with all of you! To my family and friends â thank you for your unwavering support and encouragement throughout my graduate studies. In particular, I would like to extend a special thank-you to my best friends, Nina and Dustin, for the endless food, wine, and editing. Lastly, thank-you to the UBC Department of Family Medicine for providing assistance in recruitment of medical residents, and to the Social Sciences and Humanities Research Council (SSHRC), Killam, and the UBC Provost Research Allowance (Dr. Cynthia Mathieson) for funding this research. xi Dedication In memory of Sydney Bednarik 1 CHAPTER 1 Introduction Physicians and other healthcare professionals are often tasked with the difficult job of identifying and treating patients experiencing physical pain. In fact, pain has been identified as the most common reason for seeking medical attention (e.g., Cordell et al., 2002; Daubresse et al., 2013, Varrassi et al., 2010), with as many as 70% of emergency room patients reporting pain as their primary concern (Ducharme et al., 2008; Lewén, Gardulf, & Nilsson, 2010; Puntillo, Neighbor, O'Neil, & Nixon, 2003). Despite the pervasiveness of pain-related visits, researchers have reported that 60%-80% of patients in pain are often left undertreated (Curtis & Morrell, 2006; Decosterd et al., 2007; Pines & Hollander, 2008; Stalnikowicz, Mahamid, Kaspi, & Brezis, 2005; Todd et al., 2007). For example, in a large-scale study conducted across 20 emergency departments in Canada and the United States, it was found that only 60% of patients reporting moderate to severe pain received analgesics (Todd et al., 2007). Moreover, 74% of these emergency room patients were discharged in a state of moderate to severe pain. In another study, nearly half (49%) of all patients reported that their pain was not relieved upon discharge (Guru & Dubinsky, 2000). Importantly, the inadequate treatment of pain can bear long-term negative consequences for patients, including increased rates of depression, anxiety, sleep disturbances, and reduced quality of life (Herr & Garand, 2001; Jones & Macfarlane, 2005; Sinatra, 2010). As such, there is a clear need to identify factors associated with effective pain treatment. One factor consistently found to play a crucial role in healthcare outcomes is empathy - a multidimensional concept with both affective and cognitive dimensions relating to the understanding of othersâ emotional states (Mercer & Reynolds, 2002). Empathy in healthcare workers has been linked to several important aspects of care, including increased patient 2 satisfaction and adherence, decreased patient anxiety and distress, and improved diagnostic accuracy and clinical outcomes (Derksen, Bensing, & Lagro-Janssen, 2013; Mercer & Reynolds, 2002). Based on these findings, one could reason that increasing physiciansâ empathy may improve the treatment of patients in pain. Increased levels of empathy, however, may also raise the risk of aversive outcomes for the treating physician. Vicarious exposure to othersâ pain has been found to activate neural regions of the brain associated with the affective and sensory components of pain. The cerebral overlap between the perception of pain in oneself and others, or sensorimotor resonance, is positively associated with empathy (Avenanti et al., 2009). In other words, individuals who report higher levels of empathy exhibit a stronger cortical response to another's pain (Avenanti et al., 2009; Marcoux et al., 2013; Vachon-Presseau et al., 2011). This shared neural response, however, has also been found to trigger negative emotions in the perceiver (Decety, 2011; Decety & Lamm, 2006; Lamm, Batson, & Decety, 2007). Consequently, physiciansâ repeated exposure to the suffering of others may result in personal distress, which has been associated with decreases in performance and health, including increased medical errors, emotional burnout, traumatic stress, and a low sense of accomplishment (Gleichgerrcht & Decety, 2013; West et al., 2006). Moreover, these feelings of distress often motivate self-directed responses in the perceiver aimed at reducing personal discomfort as opposed to reducing othersâ suffering (Batson, 1991; Cialdini et al., 1987; Tice, Bratslavsky, & Baumeister, 2001). As such, highly empathic physicians may risk of over-identifying with patients, potentially triggering counter-productive or harmful medical practice. Together, the aforementioned findings raise an important question - how can physicians effectively deal with othersâ pain without experiencing negative consequences 3 themselves? As described by Newton (2013), physicians walk a fine empathic line; while a level of emotional detachment from their patient is required to foster objectivity and prevent burnout, physicians risk inhibiting their ability to identify with the patient if they become too hardened. He concludes that although empathy is essential for creating a positive physician-patient relationship, lower levels of affective empathy may allow physicians to maintain an optimal degree of detachment, facilitating objective and efficient care. In a recent study, Coll, Grégoire, Eugène, and Jackson (2017) investigated the hypothesis that empathic detachment (i.e., less personal distress towards othersâ suffering) would facilitate healthcare professionalsâ prosocial treatment of patients in pain. Specifically, the researchers examined the relationships between four distinct components of empathy (i.e., perspective taking, empathic concern, personal distress, and fantasy) and helping behaviour in a sample of healthcare and non-healthcare professionals. Overall, it was found that healthcare professionals offered significantly more help to patients in pain than non-healthcare professionals and that healthcare providers reported lower levels of personal distress in response to othersâ suffering. Moreover, personal distress was found to be negatively correlated with prosocial behaviour. Interestingly, the other three components of empathy were not significantly related to helping behaviour. Together, these findings suggest that lower levels of personal distress may increase individualsâ propensity to alleviate othersâ pain, adding support to the argument that empathic detachment can be beneficial in the context of pain treatment. Conversely, other research has identified personal distress as an essential precursor to prosocial behaviour (Cialdini et al., 1987; Hein et al., 2011). For example, Cialdini et al. (1987) found that highly empathic participants engaged in more helping behaviour than their 4 low-empathy counterparts, but only when accompanied by increased feelings of personal distress (i.e., sadness). Moreover, in a second experiment, the researchers found that the empathic participants only exhibited increased helping behaviour when they believed their mood could be improved as a consequence. In a more recent study, Ashar et al. (2016) found that higher levels of personal distress predicted greater helping behaviour, even when accounting for sympathetic feelings. Other studies have found that increased levels of self-reported distress and aversive physiological arousal (i.e., increased skin conductance) in response to othersâ suffering were predictive of costly helping (Hein et al., 2011; Zahedzadeh, Barraza & Zak, 2017). For example, Hein et al. (2011) found that participants who exhibited increased autonomic arousal (i.e., heightened skin conductance response) when viewing others in pain, were subsequently more likely to volunteer to receive a painful stimulation in place of a confederate. Further, engaging in this costly behaviour was more likely to occur as the congruence between participantsâ autonomic arousal during self-pain (direct) and other-pain (vicarious) increased. From these findings, the researchers concluded that costly helping is more likely to occur when the empathy-related autonomic response elicited by othersâ pain is not only strong but also resembles the level of arousal the helper would experience when in a similar state. The results mentioned above, when taken together, suggest that enhanced treatment of patients in pain may be related to higher levels of personal distress. The existing literature provides empirical support for two opposing positions relating to role of personal distress in the willingness to alleviate othersâ pain. The presence of contradictory and occasionally weak associations may indicate a curvilinear relationship 5 between personal distress and prosocial behaviour, or the need to elucidate moderating variables. 1.1 Potential Moderating Variables 1.1.1 Pain Perception When examining the influence of empathy on prosocial responses to othersâ suffering, a crucial factor to consider is the vicarious perception of pain. The ability to respond to othersâ suffering in an empathic, helpful, or prosocial way hinges on oneâs capacity to first identify that the sufferer is experiencing pain (Prkachin, Kaseweter & Browne, 2015). The personal experience of pain (i.e., first-person pain) often elicits observable changes in the sufferersâ vocalizations, facial expressions, or body movements (Prkachin et al., 2015). While some of these behavioural changes serve to protect or alleviate pain (i.e., nociceptive withdrawal), others are primarily communicative, such as signaling a need for help (i.e., crying). The process of detecting these cues in others is often referred to as third-person pain perception. Research has shown that considerable variability exists in the perception of third-person pain. For example, when perceiving othersâ pain, observers can either detect or fail to detect the pain expression - a measure of perceptual sensitivity (Deyo, Prkachin & Mercer, 2004; Hadjistavropoulos et al., 2011; Prkachin et al., 2015). Moreover, once detected, observers can either accurately evaluate the level of pain, or misinterpret it, leading to response bias (i.e., an overestimation or underestimation of othersâ pain; Prkachin et al., 2015; Prkachin, Solomon & Ross, 2007). The accuracy of pain detection has been found to be influenced by characteristics of the sufferer (e.g., the intensity of facial expression), but also by characteristics of the observer, such as age, gender, congenital disabilities, or 6 personality traits (Calder et al., 2003; Danziger, Prkachin, & Willer, 2006; Prkachin et al., 2015). Importantly, a substantial body of research has revealed a prominent pain underestimation bias in healthcare professionals (Prkachin et al., 2007). That is, the pain judgments made by health professionals tend to be lower than those of the patients themselves (e.g., Prkachin, 2007; Solomon, 2001). It is plausible that this bias may influence a physicianâs response to patients in pain. For example, Prkachin et al. (2007) argue that healthcare professionalsâ assessment of patientsâ suffering is likely a critical determinant of the level of intervention or pain relief they provide. This theorized link between physiciansâ vicarious pain perception and treatment behaviour would be particularly true in cases where the patient is unable to self-report their level of pain (e.g., patients with dementia; Ruben et al., 2015). In line with this hypothesis, research has found evidence to suggest that congruence between patient-physician ratings of patientsâ pain is associated with higher levels of patient satisfaction and improved health outcomes (e.g., Cleeland et al., 1997; DiMatteo & Martin, 2002; Hall, 2011). For the aforementioned reasons, it is conceivable that accuracy in third-person pain perception could moderate the relationship between empathy and prosocial behaviour. For example, it may be that lower levels of personal distress increase prosocial behaviour, but only when accompanied by high levels of third-person pain sensitivity. Conversely, for individuals who are unable to detect facial expressions of pain accurately, personal distress may serve as an important motivator for helping behaviour (as suggested by the findings of Cialdini et al., 1987). In their investigation of the relationship between empathy and prosocial behaviour, Coll et al. (2015) did find that pain intensity ratings were strongly and positively correlated with prosocial behaviour. It should be noted, however, that the pain ratings were 7 examined as an independent predictor (i.e., not as a moderating variable), and a distinction was not made between the two independent parameters of pain perception (i.e., sensitivity and response bias). As such, further investigation into the potential moderating influence of individual variability in third-person pain sensitivity is warranted. 1.1.2 Emotion Regulation A second potential moderating variable identified in the literature is emotion regulation, which is understood as the capacity to monitor and modify the intensity and expression of emotions (Thomson, 1994). Eisenberg and Fabes (1992) proposed a theoretical model linking individual differences in both the intensity and regulation of emotions to prosocial behaviour. Specifically, the researchers posited that while the vicarious perception of distress can result in emotional arousal in the perceiver, the effect of that arousal on prosocial behaviour can be moderated by emotion regulation. According to their model, for individuals who are able to regulate their emotions effectively, the level of personal distress should not be an important determinant of their prosocial behaviour. Lockwood, Seara-Cardoso, and Viding (2014) empirically examined the hypothesis that individual differences in the degree and type of emotion regulation moderates the degree to which empathy is associated with prosocial behaviour. To do so, a sample of healthy individuals completed self-report measures of empathy, emotion regulation (i.e., cognitive reappraisal), and prosocial tendencies. In general, affective empathy was positively related to prosocial tendencies. Consistent with the Eisenberg and Fabes (1992) model, this association was nonexistent for individuals with a high tendency to cognitively reappraise. That is, participants high in cognitive reappraisal demonstrated similar levels of prosocial behaviour regardless of their level of affective empathy. Surprisingly, prosocial behaviour was highest 8 for individuals low in cognitive reappraisal and high in affective empathy. This finding is in contrast to the Eisenberg and Fabes (1992) model, which predicts that individuals high in emotional intensity and low in emotion regulation would be unlikely to engage in prosocial behaviour and may even exhibit antisocial behaviour. One possible explanation for the discrepancy is that prosocial proclivity was assessed using the Prosocial Tendencies Measure (PTM; Carlo & Randall, 2002), a self-report scale designed to measure an individualâs tendency towards prosocial behaviour, rather than the actual frequency or degree of past prosocial behaviours. Moreover, this scale has been found to be positively related to social desirability, with individuals who exhibit a tendency to portray themselves positively, reporting greater prosocial tendencies. It should also be noted that the moderating influence of emotion regulation could depend on the type of affective empathy (e.g., empathic concern and personal distress) or the context of prosocial behaviour â distinctions that were not made in the Lockwood et al. (2014) study, thus necessitating further investigation. 1.1.3 Psychopathic Traits Although often considered in the context of antisocial behaviour, psychopathic traits may be particularly relevant when examining the relationship between empathy and prosocial behaviour. Due to the strong association with criminality, one may presume a link between psychopathy and decreased rates of prosocial behaviour; however, it is important to note that prosocial and antisocial behaviour are not always inversely related (White, 2014). Indeed, in a recent study, researchers found that police recruits scored significantly higher than a sample of criminal offenders on the fearlessness dominance and coldheartedness components of psychopathy (Falkenbach, Balash, Tsoukalas, Stern, & Lilienfeld, 2018). The researchers posited that these elevated psychopathic traits might be beneficial in the context 9 of police work. For example, the presence of coldheartedness may promote emotional detachment, facilitating composure and prosocial behaviour under emotionally challenging situations â benefits that could conceivably extend to the context of healthcare. Additionally, it has been suggested that psychopathic traits may influence the type of helping behaviour in which an individual chooses to engage. For example, researchers found that although psychopathic traits were negatively associated with altruistic prosocial behaviour, they were positively associated with public prosocial behaviour (White, 2014). Consequently, these traits may be positively related to helping behaviour under certain conditions. Of note, these associations were mediated by a lack of empathy. A defining characteristic of psychopathy is a deficit in empathy, resulting in a lack of regard for the pain of others. Importantly, however, findings from studies suggest that components of psychopathy may differentially interact with facets of empathy (e.g., Decety, Chen, Harenski & Kiehl, 2013; Johnson et al., 2014; Reji, 2019; Sörman et al., 2016). For example, in a recent study, it was found that while both the self-centered impulsivity and coldheartedness components of psychopathy were negatively related to empathic concern and perspective taking, opposing patterns emerged for personal distress (Reji, 2019). Specifically, self-centered impulsivity was positively related to personal distress, whereas coldheartedness was negatively associated. In other words, both individuals scoring high in coldheartedness and self-centered impulsivity may exhibit lower levels of empathic concern and perspective taking in response to othersâ pain. Due to their inverse associations with personal distress, however, these traits could conceivably have differential influences on prosociality. Importantly, psychopathic traits have also been linked with differences in perceptual sensitivity to othersâ pain (e.g., Caes et al., 2012; Kaseweter, 2015; Kaseweter, Rose, 10 Bednarik, & Woodworth, 2020). Specifically, individuals scoring high in psychopathic traits (particularly callous affect) have been found to be less accurate at identifying pain in others (e.g., Caes et al., 2012; Kaseweter, 2015). Moreover, in a recent study, psychopathic traits were found to predict implicit attentional biases to facial expressions of distress (Kaseweter et al., 2020). Specifically, it was found that callous-affective traits were related to decreased attention to expressions of pain (i.e., pre-attentive avoidance), whereas criminal tendencies and erratic lifestyle were related to increased attention to pain (Kaseweter et al., 2020). These findings suggest that dimensions of psychopathy (i.e., coldheartedness and impulsivity) may differentially relate to the perceptual processing of othersâ pain â potentially influencing rates of prosocial behaviour. Similarly, dimensions of psychopathy have been found to exhibit divergent relationships with emotion dysregulation (e.g., Donhue, McClure, & Moon, 2014; Miller et al., 2010). For example, Donahue et al. (2014) found that the fearless dominance component of psychopathy was negatively related to emotional regulation difficulties, whereas the self-centered impulsivity component had a strong, positive association with emotional dysregulation. Together, these findings provide ample reason to believe that psychopathic traits may moderate the relationship between empathy and prosocial behaviour. 1.1.4 Personal Cost of Helping In addition to examining individual difference characteristics, it is important to consider how the personal cost of helping may influence the empathy-prosocial relationship. For example, when physicians choose to offer help to patients in pain, they may be doing so at the personal cost of increasing their workload. Two prominent theories of costly helping have emerged â one pointing to egoistic motivations and the other to altruistic motivations 11 (FeldmanHall, Dalgleish, Evans, & Mobbs, 2015). The first hypothesis is the arousal-reduction or cost-reward hypothesis, which proposes that altruistic behaviour is motivated by the desire to alleviate personal discomfort when observing othersâ suffering (Batson, 1987; Cialdini et al., 1987; Dovidio & Penner, 2004; Hoffman, 1991). The second hypothesis is the empathyâaltruism hypothesis, which states that altruistic behaviour is evoked by other-oriented empathy (i.e., empathic concern; Batson et al., 1993). FeldmanHall et al. (2015) recently examined both theories of altruism using a Pain vs Gain task. Participants underwent a series of trials in which they could increase their financial gain at the expense of ostensibly administering harmful shocks to another participant. Opting not to deliver a shock, and thus deciding to forgo the money, was characterized as a costly altruistic decision. The researchers found that costly helping was positively associated with empathic concern, but not with personal distress, supporting the empathy-altruism theory. This finding suggests that when the personal cost of helping is high for physicians, empathic concern may increase prosocial behaviour. In the Coll et al. (2017) study, the researchers employed a novel prosocial task designed to capture this costly helping with which physicians are often faced. In the paradigm, participants were presented with photos of patients in varying levels of pain and asked to indicate how much help they would offer. They were warned that offering help would be associated with an increased workload, thus increasing their experimental time. Participants who chose to offer the full amount of help to all of the patients would do so at the risk of increasing the task duration by 19 minutes. As stated previously, the researchers found that this costly help was negatively correlated with personal distress â a surprising finding, given both theories of costly altruism. The authors noted, however, that in their 12 paradigm the consequence of offering more help was relatively mild. Although the duration of the overall task could have been increased by several minutes depending on the level of help offered, the decision to offer the full amount of help on each individual trial only resulted in a 20-second waiting period. Consequently, increasing the personal cost of helping may have led to different results. In addition to increased workload, physiciansâ help may come at a financial cost. Numerous studies have demonstrated that the payment method of physicians is an important contributor to the efficiency, quality, and quantity of health care delivery (e.g., Battista & Spitzer, 1983; Forsberg, Axelsson, & Arnetz, 2001; Glazier, Klein-Geltink, Kopp, & Sibley, 2009; Godager & Wiesen, 2013; Rudmik, Wranik, & Rudisill-Michaelsen, 2014; Wranik, 2012). Physician remuneration can be divided into three pure payment methods: salary, capitation, and fee-for-service (FFS; Lagarde & Blaauw, 2017; Rudmik et al., 2014). Salary is a time-based payment method in which physicians are paid a fixed amount depending on the amount of time they work. Capitation is a patient-based payment, in which remuneration is dependent on the number of patients seen during a time-period. Similarly, FFS remuneration is based on the number of services rendered. Physician payment method varies by province and specialty in Canada (Wranik & Durier-Copp, 2010). For example, Alberta is the only province to offer pure capitation contracts for family physicians; whereas, salary contracts are most common in rural areas, such as the Northwest Territories and Newfoundland. British Columbia offers a blended model, which combines FFS and capitation payment. Capitation methods are designed to increase efficiency by providing physicians with an incentive to increase the number of patients served. This payment method, however, does 13 not provide an incentive for quality or quantity of patient care and, as such, has been associated with more patient referrals, increased patient visits to the emergency department, and reduced after-hour care (Glazier, Klein-Geltink, Kopp, & Sibley, 2009; Iversen & Luras, 2000). Conversely, FFS provides an incentive to offer more healthcare services to the patient, and consequently has been associated with higher levels of patient satisfaction (e.g., Sorbero, Dick, Zwanziger, Mukamel, & Weyl, 2003). This payment method, however, comes at the cost of increased healthcare expenditures (Devlin & Sarma, 2008). Lastly, although salary offers no incentive for the quantity of care, often resulting in fewer patients seen, it has been found to promote quality of care (Gosden, Sibbald, Williams, Petchey, & Leese, 2003; Lagarde & Blaauw, 2017). Together, these findings suggest that the type of payment may influence the personal cost associated with helping, thus contributing to the relationship between empathy and prosocial behaviour. For example, in capitation-based scenarios, physicians who choose to offer help would be doing so at the personal cost of reducing their potential income. As previously mentioned, FeldmanHall et al. (2015) found that when faced with a personal financial cost, empathic concern was predictive of helping behaviour. This perceived loss in income may represent a greater personal cost than the wait times employed by the Coll et al. (2017) study, contributing to the discrepancy in findings. As such, the moderating role of the personal cost associated with helping should be further explored. 1.2 Project Summary Despite a growing body of evidence indicating that personal distress is predictive of physiciansâ prosocial treatment of patients in pain, the intricacies of this relationship remain unclear. While some research indicates positive correlations between high levels of personal 14 distress and helping behaviour, other studies indicate the opposite, suggesting low levels of personal distress are related to increased willingness to help. This discrepancy in the literature may be reflective of a curvilinear relationship between personal distress and the prosocial treatment of patients in pain; in other words, an optimal level of personal distress may exist for responding to othersâ suffering. Additionally, the literature presented provides reason to believe that differences in personality characteristics and the context in which the help occurs may moderate the relationship between empathy and prosocial behaviour. Specifically, variability in empathy and prosocial behaviour has been linked with differences in pain perception, emotion regulation, and psychopathy. Moreover, the literature suggests that the personal cost associated with helping may explain some of the heterogeneity in prosocial behaviour, as well as moderate the association with empathy. Researchers to date, however, have not yet examined these factors together to determine the extent to which they moderate the relationship between personal distress and helping behaviour towards patients in pain. In order to address these important gaps in the existing literature, two studies were conducted to clarify the nature of the relationship between empathy and prosocial behaviour towards persons in pain, as well as elucidate potential moderating variables. Specifically, Study 1 and Study 2 sought to answer the following questions regarding the treatment of patients in pain: 1) Empathy and Prosocial Behaviour: Is personal distress the strongest predictor of prosocial behaviour when compared to the other components of empathy (i.e., perspective taking, fantasy, and empathic concern)? Is the relationship between personal distress and prosocial behaviour curvilinear? 15 2) Moderation â Emotional Response Profiles: Does personal distress interact with third-person pain perception, emotion regulation, and psychopathic traits to form emotional response patterns? And, if so, do these emotional response profiles differentially influence willingness to engage in prosocial helping behaviour? 3) Moderation â Personal Cost of Helping: Is the relationship between empathy and prosocial helping behaviour modified by the personal cost of helping (i.e., type of remuneration)? 4) Generalizability: Will the emotional response profiles replicate in a separate sample of undergraduate students, and extend to a sample of healthcare workers? 5) Comparing Predictors: Which is the best predictor of prosocial behaviour: emotional response profiles, personal cost of helping, or participant type (i.e., student or medical resident)? Study 1 addressed research questions one and two in a sample of undergraduate students. Study 2 addressed research questions three to five and included an additional sample of medical residents. As highlighted by Lagarde and Blaauw (2017), an important issue within many health studies is the experimental designs, which often lack ecological validity. Specifically, these laboratory studies regularly use paradigms in which participants are asked to choose whether to deliver hypothetical health services to a fictional patient. Lagarde and Blaauw (2017) state that these âchosen effortâ paradigms fail to capture important aspects of real healthcare decisions where effort is not hypothetical or always negative. To circumvent this problem, helping behaviour in both Study 1 and Study 2 was assessed by employing a modified version of the Coll et al. (2017) prosocial paradigm. The task designed by Coll et al. is 16 unique in that the amount of help offered corresponded with increased time spent with a patient (i.e., a cost of helping) and also with visual feedback depicting a decrease in patientsâ pain (i.e., a benefit of helping). As previously noted, however, the consequence of helping each patient was relatively minor. For this reason, the decision to help in the task employed in the present studies was associated with longer wait-times, as well as a âreal-effortâ task in which participants were asked to respond to a question during the waiting period. Further, remuneration conditions were added in the second study to capture the personal financial cost associated with real healthcare decisions. It should be noted that several other important modifications were made from the Coll et al. (2017) study. First, medical residents were examined, incorporating a type of healthcare professional not included in the study by Coll et al. (2017). Additionally, third-person pain perception was examined using signal detection methods allowing for the examination of two independent parameters: pain sensitivity and response bias. Further, pain perception was examined in a moderating role, along with emotion regulation, psychopathic traits, and personal cost of helping. Importantly, interaction effects were explored using latent profile analysis (LPA). Researchers have traditionally utilized generalized linear models, such as multiple regression, to examine moderation. For example, moderation is often assessed by including an interaction term between predictor and moderating variables in a regression model. Significant interaction terms suggest that the linear relationship between the predictor and outcome variable varies depending on the level of the moderator variable. Although these âvariable-centeredâ approaches can be useful in examining interaction effects, they have limitations that are important to consider (Bauer & Shanahan, 2007; Lanza & Rhoades, 17 2013). Notably, when several variables are explored as potential moderators, Type I error rates are inflated due to the number of significance tests required (Lanza & Rhoads, 2013). Moreover, the number of lower-order and higher-order interaction terms increases exponentially with the inclusion of each moderator, making significant interactions exceedingly more difficult to both detect (i.e., due to lack of statistical power) and interpret (Bauer & Shanahan, 2007). Further, although plotting simple slopes may facilitate interpretation of complex interactions, the combinations of the moderating variables displayed in the plot (e.g., one standard deviation above the mean on each moderator) may not be representative of common configurations found within the sample (Bauer & Shanahan, 2007). As such, conclusions drawn may not be relevant to the population of interest. To circumvent the limitations of traditional moderated regression, LPA was used to explore potential complex interactions among empathy, third-person pain perception, emotion regulation, and psychopathic traits. LPA is a âperson-centeredâ approach that can be used to simultaneously examine multiple variables to identify individual response patterns that may be unobservable when analyzed by linear models. The pattern profiles derived by LPA can then be used in follow-up analyses to determine whether individuals characterized by these response patterns differ on an outcome variable (e.g., prosocial helping behaviour). 18 CHAPTER 2 Study 1 2.1 Study 1 Overview The primary aim of this study was to explore the relationship between empathy and helping behaviour in a sample of undergraduate students using a modified version of the prosocial task employed by Coll et al. (2017). Specifically, this study was used to investigate the possibility of a curvilinear relationship between empathy and helping behaviour, and to examine the potential moderating role of individual differences in characteristics pertaining to emotional processing. It was hypothesized that participantsâ level of personal distress would emerge as the strongest empathy predictor of prosocial behaviour and that this relationship would be curvilinear. Secondly, it was hypothesized the relationship between empathy and prosocial behaviour would be moderated by variability in third-person pain perception, emotion regulation, and psychopathic traits. Specifically, it was hypothesized that these variables would interact to form emotional response profiles, and that that profiles would distinguish engagement in helping behaviour. 2.2 Participants Undergraduate students (N = 283) at the University of British Columbia Okanagan (UBCO) were recruited using the Department of Psychologyâs SONA online research system. The sample consisted of 180 women and 103 men, with a mean age of 20.46 years (SD = 2.68). Participants predominantly self-identified as White (65.4%), followed by Asian (20.5%), Other (11%), Black (1.7%), and Latino/Latina (1.4%). In exchange for their involvement, participants were awarded one course credit. All participants were required to have normal or corrected vision, and to be fluent in reading English. Additionally, due to the 19 nature of the study, exclusion criteria included any reported history of a pain-related disorder (e.g., chronic pain). 2.3 Materials Stimuli presentation and recording of responses was carried out using Qualtrics (Qualtrics, Provo, UT) â an online survey system. Participants were given the option of completing the study online (n = 182) or in a laboratory room at UBCO (n = 101). 2.3.1 Assessment of Prosocial Behavior Prosocial behaviour was assessed using a modified version of the prosocial task previously employed by Coll et al. (2017). Although the task procedures deviated from that of the original study, the visual stimuli were the same. 2.3.1.1 Visual Stimuli. Visual stimuli consisted of 90 still-image frames, previously extracted by Coll et al. (2017) from the UNBC-McMaster Shoulder Pain Archive (Lucey et al., 2011; Prkachin & Solomon, 2008). This database contains video recordings of real patients (63 males, 66 females) with shoulder injuries who participated in a study of shoulder pain (Lucey, Cohn, Prkachin, Solomon, & Mathews, 2011). Recordings were made during active and passive movements of patientsâ unaffected and affected shoulders, generating a database of spontaneous facial expressions ranging from no pain to high pain. In both tasks, the limb (affected or unaffected) was moved until the maximum range was achieved. The active test differed from the passive test in that it was under the patientsâ control and performed in a standing position. The passive test, in contrast, was performed by a physiotherapist with the patient resting in a supine position. The frames selected from the database were obtained from the video recordings of 20 patients (10 males, 10 females; 14 pain stimuli, 6 neutral stimuli). For pain stimuli, six 20 frames were extracted from each video clip: a test frame containing the peak expression of pain, four feedback frames depicting gradual decreases in the pain expression, and a neutral frame expressing no pain. For neutral stimuli, only a neutral frame displaying no pain was selected. All frames were drawn from the passive (vs active) range-of-motion tests. 2.3.1.2 Help Ratings. Participants rated the amount of time they wished to spend helping each patient using self-report Visual Analog Scales (VAS) following the presentation of each test frame. The scale appeared on the computer screen as a grey horizontal line, anchored with the verbal descriptors no help and maximum time allocated. Participants made ratings by sliding a movable bar to the appropriate position on the scale, or by simply using the mouse to left-click on the desired location. To avoid priming participants, the starting position of the slider was offset, such that it appeared to the left of the no help anchor. Therefore, participants were required to actively move the bar from the starting position to record a rating. 2.3.2 Assessment of Third-Person Pain Perception Participantsâ perception of othersâ pain was evaluated by adapting a third-person pain perception task used in a previous study (Kaseweter, 2015). This task has been found to successfully measure individual differences in third-person pain sensitivity and response bias. 2.3.2.1 Visual Stimuli. This task is comprised of 60 one-second video clips depicting facial expressions of individuals experiencing differing levels of pain (Kaseweter, 2015). These facial expressions employed in this task were previously selected from the UNBC-McMaster Shoulder Pain Expression Archive â the same database used in the prosocial behaviour task (Prkachin & Solomon, 2008). Stimuli from this archive has been used 21 extensively in the study of third-person pain perception (e.g., Coll, Grégoire, Prkachin, & Jackson, 2016; Rash, Prkachin, & Campbell, 2015; Schäfer, Prkachin, Kaseweter, & de C Williams, 2016). The video recordings within the database have been rated for the amount of pain expressed using a system based on Ekman and Friesenâs (1978) Facial Action Coding System. Facial expressions of pain are largely comprised of four movements: brow lowering, orbit tightening (narrowing of the eye apertures and raising of the cheeks), levator tightening (raising of the upper lip and/or nose wrinkling), and eye closure (Prkachin, 1992). These actions have been found to vary in intensity, positively correlating with the subjective experience of pain (Prkachin, Berzins & Mercer, 1994; Prkachin & Mercer, 1989; Prkachin & Solomon, 2008). All video recordings have been scored for brow lowering, orbit tightening, and levator tightening using an intensity scale ranging from 1 (a trace of the action) to 5 (the action occurs at maximum intensity). Eye closure was rated on a scale from 0 (absent) to 1 (present). Scores for each action were summed to create a total pain expression score that ranges from 0 to 16. Excerpts are considered to display high pain if the total score is six or greater, moderate pain if the score is four or five, low pain if the score is two or three, and no pain if the score is zero. Using the aforementioned criteria, 60 one-second video clips were extracted from the database, with equal numbers falling into three categories of pain: moderate pain, low pain, and no pain (Kaseweter, 2015). High intensity pain clips were excluded to produce a test of sufficient difficulty to capture individual differences in perceptual sensitivity and response bias. To minimize disparities in visual angle, only video clips from the active range-of-motion tests were selected, which provided a relatively direct, frontal view of the patients' 22 faces. This visual angle is in contrast to the prosocial behaviour task, which used expressions from the passive tests. Moreover, each intensity level contains equal numbers of males and females. 2.3.2.3 Pain Ratings. Participants rated the perceived intensity of pain displayed in each video clip using VAS. Considerable empirical support has been found for the validity of VAS in pain measurement, indicating high sensitivity, reliability, and convergent validity with other self-report as well as observed pain measures (Jensen & Karoly, 2011; Price, McGrath, Rafii & Buckingham, 1983). The scale appeared on the computer screen as a grey, horizontal line, anchored with the verbal descriptors no pain and extremely strong pain. Participants made ratings by sliding a movable bar to the appropriate position on the scale, or by left-clicking with the mouse on the desired location. As in the prosocial behaviour task, the starting position of the slider was offset to the left of the no pain anchor to avoid priming participants and to encourage active ratings. 2.3.3 Self-Report Measures 2.3.3.1 Demographics. A standard demographics questionnaire was used to obtain information on each participant. Specifically, data collected included participantsâ gender, ethnicity, and age (Appendix A). 2.3.3.2 Empathy. Empathy was assessed using the Interpersonal Reactivity Index (IRI; Davis, 1980; Appendix B). The IRI is a 28-item, multidimensional measure of trait empathy, consisting of four subscales: (a) perspective taking; (b) fantasy; (c) empathic concern; and (d) personal distress. The perspective taking subscale assesses an individualâs tendency to adopt the point of view of other people, whereas the fantasy subscale assesses an 23 individual's tendency to identify with the feelings and actions of fictional characters. These two scales relate to the cognitive features of empathy. The empathic concern subscale assesses an individual's tendency to experience compassion for other people, and the personal distress subscale assesses an individualâs tendency to experience emotional discomfort in response to the distress of others. These two subscales capture other- and self- oriented features of affective empathy, respectively. Each subscale is composed of seven items, rated on a 5-point Likert scale from 1 (does not describe me well) to 5 (describes me well). Subscale scores range from 7-35, with higher scores reflecting greater levels of empathy. Although the IRI was not originally designed for use in healthcare settings, the scale has been used in numerous studies to evaluate empathy in healthcare workers and has shown good internal consistency and convergent validity within these contexts (Beckman, Reed, Shanafelt & West, 2012; Evans, Stanley & Burrows, 1993; Gleichgerrcht & Decety, 2013; McFarland, Malone, & Roth, 2017; Yarnold, Bryant, Nightingale & Martin, 1996). 2.3.3.4 Emotion Regulation. Emotion regulation was measured using the Emotion Regulation Questionnaire (ERQ; Gross & John, 2003; Appendix C). The ERQ is a 10-item self-report questionnaire used to assess individuals' tendency to regulate emotions using two strategies: cognitive reappraisal and expression suppression. The cognitive reappraisal subscale is comprised of six items and measures the tendency to reframe the meaning of emotional experiences; whereas, expression suppression is measured by four items and assesses the tendency to inhibit emotionally-expressive behaviour. Items are rated on a 7-point scale from 1 (strongly disagree) to 7 (strongly agree). The ERQ has been found to demonstrate good construct validity and internal consistency (Gross & John, 2003). 24 2.3.3.5 Psychopathic Characteristics. Psychopathic personality traits were assessed using the abbreviated version of the Psychopathic Personality Inventory-Revised (PPI-R-40; Eisenbarth, Lilienfeld & Yarkoni, 2014; Appendix D). This 40-item self-report scale is designed to measure psychopathic traits on a continuum in noncriminal populations. Participants respond to each item on a scale of 1 (Mostly False) to 4 (Mostly True), generating eight subscale scores: (a) social influence; (b) stress immunity; (c) fearlessness; (d) Machiavellian egocentricity; (e) rebellious nonconformity; (f) carefree nonplanfulness; (g) blame externalization; and (h) coldheartedness. These subscales can then be collapsed into two higher-order dimensions of psychopathy: fearless dominance and self-centered impulsivity. Fearless dominance captures the interpersonal-affective features of psychopathy and includes subscales one through three; whereas, self-centered impulsivity assesses the behavioural-lifestyle features of psychopathy and includes subscales four through seven. The coldheartedness subscale does not load onto either dimension and is considered a separate construct (Benning, Patrick, Hicks, Blonigen & Krueger, 2003; Ruchensky, Edens, Donnellan & Witt, 2017). The subscale and composite scores of the PPI-R-40 have been found to strongly correlate with those of the original 156-item version (Eisenbarth et al., 2015; Ruchensky et al., 2017). Moreover, this abbreviated scale has been found to demonstrate good internal consistency and construct validity, comparable to that of the full-length scale (Eisenbarth et al., 2015; Ruchensky et al., 2017). 2.4 Procedure Participants were given the option of scheduling an in-person laboratory session or completing the study online using the Department of Psychologyâs SONA online research system. Regardless of study setting, all participants completed the study using the same 25 Qualtrics hyperlink. Upon opening the hyperlink, participants were presented with a consent form to which they had to agree to proceed with the study. Participants were then directed to the prosocial behaviour task, followed by the third-person pain perception task and self-report measures (i.e., IRI, ERQ, and PPI-R-40). Task order was fixed in this sequence to avoid priming participants prior to the prosocial behaviour task. 2.4.1 Prosocial Behaviour Task Participants began this task by reading a scripted set of instructions, familiarizing them with the procedure and rating scale. They were asked to place themselves in the role of a healthcare professional in charge of treating pain in patients. They were told that patientsâ pain would decrease as the amount of time they offered to help increased and that they would receive visual feedback displaying the effects of the level of help provided. To increase ecological validity and variability in responding, participants were reminded that offering help to patients would be associated with an increased workload, as well as increased time to complete the task. Thus, engaging in prosocial behaviour had the consequence of prolonging a participantâs session. During this task, participants completed 20 experimental trials in which they were queried about the amount of help they would offer each patient (See Figure 1). Each trial began with a fixation cross presented for 2s, followed by a test frame displayed for 2.5s. Each test frame showed a facial expression of a patient displaying either pain (pain stimuli) or no pain (neutral stimuli). Participants were then asked to rate the amount of help they wished to offer that patient. The VAS sliding scale appeared on the screen for 5s, with the verbal descriptors no help and maximum time allowed. Following each rating, participants then entered a waiting period â the length of which corresponded to the level of help offered. 26 For instance, if the participant offered little or no help (â¤ 20% of the VAS), the waiting screen was presented for 1s, while more help resulted in longer waiting times (i.e., 21-39% = 15s; 41-59% = 30s; 60-79% = 45s; â¥ 80% = 60s). As such, participants who chose to offer the maximum help to every patient would have increased the task duration by approximately 19 minutes compared to a participant who chose to offer little or no help to all patients. Although participants were aware that more help would be associated with longer wait times, the specific details of this process and the length of wait-times were not disclosed. In addition to the waiting period, participants who chose to help the patient (> 20% of the VAS) were asked to provide brief notes on how they arrived at their treatment decision. Specifically, the free-response question appeared for the duration of the waiting screen, during which time participants could type their responses. This additional task was used to increase workload when help was offered. As previously mentioned, many experimental health studies assessing prosocial behaviour lack ecological validity by failing to capture the increased effort associated with the decision to help. As such, actual behavioural demand was placed on participants (i.e., notes on treatment decisions), more closely modelling the everyday realities of physicians. After the waiting period, participants were presented with a feedback frame for 2.5s. The feedback frame was used to display the patientsâ outcome corresponding to the level of help offered. For pain trials, the feedback frame depicted the same patient from the test frame displaying a similar amount of pain if little or no help was offered (â¤ 20% of the VAS: level 1) or in gradually decreasing levels of pain according to the amount of help offered (21 to 39% of the VAS: level 2, 41 to 59 %: level 3, 60 to 79%: level 4; â¥ 80 %: neutral frame). In other words, a participant who chose to offer the maximum amount of help to a patient (80- 27 100% of the VAS) would have successfully relieved the pain of the patient and been rewarded by viewing that patient in no pain (a neutral frame). For the neutral trials (no pain), the feedback frame was always the same neutral frame as the test frame. The presentation order of the 20 trials was randomized for each participant to control for potential order-effects. Figure 1 Schematic Representation of Prosocial Behaviour Task Note. Each trial began with a fixation cross, followed by a test frame. Participants then indicated the amount of help they wanted to offer the patient using a VAS scale. A waiting period then began, the length of which varied based on the amount of help offered. During the waiting period, participants were asked to provide notes on how they arrived at their treatment decision. Lastly, a feedback frame appeared, displaying the amount of pain relief associated with the amount of help provided. Participants completed trials for 20 patients. 2.4.2 Third-Person Pain Perception Task Similar to the previous task, this task began with a scripted set of instructions, introducing participants to the new procedures and pain intensity rating scale. Specifically, participants were asked to place themselves in the role of a healthcare professional tasked with evaluating (as opposed to treating) pain in patients. During the task, participants viewed 60 one-second video clips of patients in varying amounts of pain (i.e., no, low, and moderate). The presentation of these clips was randomized for each participant to control for potential order-effects. Following each clip, participants were asked to record their judgment of the pain intensity they believed the patient was experiencing using a VAS, with the verbal descriptors no pain and extremely strong pain. 28 2.4.3 Self-Report Measures Lastly, participants were asked to complete the IRI, ERQ, and PPI-R-40. Questions from these measures appeared on the screen sequentially. Participants recorded their degree of agreement with the statements by selecting the appropriate response on the corresponding Likert scale. Following the completion of the study, participants were debriefed regarding the nature of the study (e.g., the personality traits being examined). Participants were then be assigned a course credit in exchange for their time. 2.5 Data Reduction 2.5.1 Prosocial Behaviour During the prosocial behaviour task, participants completed a total of 20 VAS help ratings. The amount of help offered was recorded as a number ranging from 0 to 100. These scores were averaged to obtain a helping score for pain and neutral trials. 2.5.2 Third-Person Pain Perception During the third-person pain perception task, participants completed 60 pain intensity ratings. These VAS ratings were recorded as scores ranging from 0 to 100 and analyzed using signal detection procedures for rating-scale tasks (e.g., McNicol, 1972; Rash et al., 2015). Signal detection analysis allows for the independent evaluation of two distinct properties of pain perception: pain sensitivity and response bias. Pain sensitivity [P(A)] refers to an individualâs ability to discriminate between levels of pain; response bias (B) refers to an individualâs propensity to infer pain. Pain sensitivity was first calculated for three levels of discrimination: between no pain and low pain expressions [P(A)NL]; between no pain and moderate pain expressions; 29 and between low pain and moderate pain expressions. To derive pain sensitivity scores for the first two levels, no-pain stimuli were considered noise, while low- and medium-pain stimuli served as levels of pain signals. In the third level, low-pain stimuli were treated as noise. Intensity VAS ratings were separated into 10 categories (e.g., 0 â 10, 11 â 20â¦91 â 100). The probability of using each category to rate noise and the two levels of signal stimuli were then calculated, the resulting probabilities of which accumulated from the highest (91 â 100) to the lowest (0 â 10) category for each stimulus. Cumulative probabilities for the noise category (e.g., no pain) were used to represent false alarms, whereas cumulative probabilities of the signal category (e.g., moderate pain) served as hits. Based on these values, measures of pain sensitivity were calculated by measuring the proportion of the area under the Receiver-Operating-Characteristic (ROC) curve representing each level of discrimination, by application of the trapezoidal rule. Specifically, the area under the curve (AUC) is divided into equal segments (i.e., at each pain interval), forming trapezoidal sections (Eng, 2005). The area of each trapezoidal section is then summed to determine the total AUC. In total, three pain sensitivity measures were derived for each participant: no-low pain; no-moderate pain; and low-moderate pain. These three measures were then averaged to create a single pain sensitivity score for each participant. Response bias was calculated by identifying the point on the intensity VAS rating scale at which the respondent is equally likely to perform a hit or a false alarm. Similar to sensitivity, a response bias score for each participant was calculated at all three levels of discrimination: no-low pain; no-moderate pain; and low-moderate pain. These three measures were then averaged to create a single pain response bias score for each participant. 30 Sensitivity scores represent an individualâs ability to discriminate between different intensities of pain expressions - the value of which can vary between 0 and 1. A sensitivity score of 1.0 indicates perfect discrimination, a score of 0.5 represents chance performance, and a score of 0 indicates no discrimination at all. Response bias scores range from 0 to 10 and correspond to the point on the scale at which participants are equally likely to rate a higher or a lower pain expression. Higher values for response bias reflect a greater likelihood of attributing pain to a patient, regardless of the level of pain expression. This score represents an individualâs tendency to systematically make ratings toward one or the other end of the rating scale. 2.5.3 Self-Report Measures All participants completed in full the IRI, ERQ, and PPI-R-40. Subscale scores on these measures were calculated to assess participantsâ level of empathy, emotion regulation, and psychopathic traits, respectively. 2.5.4 Analytic Strategy All analyses were conducted using SPSS version 23 and R version 3.6.2. First, following the methods of Coll et al. (2017), the amount of help offered to neutral stimuli was used as a measure to verify comprehension of the prosocial task and attention to stimuli. Specifically, a dependent samples t test was conducted to confirm that participants responded with higher help ratings to pain stimuli than neutral stimuli. Next, descriptive statistics for the prosocial task were explored. Specifically, a mixed-model analysis of variance (ANOVA) was used to explore the effect of patient and participant gender on help ratings. Descriptive statistics were then calculated for all predictor variables, as well as their bivariate correlations with helping behaviour. 31 To test the first research question, multiple regression was first used to examine and compare the predictive role of four components of empathy (IRI subscales) in prosocial behaviour to patients in pain. Next, to test for the presence of a curvilinear relationship between personal distress and prosocial behaviour, multiple regression with linear and quadratic components was used. To examine the second research question, LPA was conducted to detect pattern profiles in emotional responding using scores on six indicator variables: Empathic Concern, Personal Distress, Cognitive Reappraisal, and Self-Centered Impulsivity, Coldheartedness, and Pain Sensitivity. Due to the number of predictor variables in this study, only the subscales hypothesized to have the strongest moderating influence were included as indicator variables. Specifically, it was hypothesized that in order for individuals to engage in helping behaviour, low levels of personal distress would need to be accompanied by high levels of empathic concern and pain sensitivity (i.e., empathic-altruism), and high levels of personal distress would need to be accompanied by high levels of cognitive reappraisal (i.e., emotion regulation). Additionally, for individuals high in coldheartedness and self-centered impulsivity, it was hypothesized that higher levels of personal distress may be necessary to elicit prosocial helping behaviour (i.e., arousal-reduction). Exploratory follow-up analyses were then conducted to determine whether the profiles differed on the predictor variables not included in the LPA model. Lastly, an independent analysis of variance (ANOVA) was used to test whether these profiles were differentially related to helping behaviour. LPA was performed in R using the âtidyLPAâ package (Rosenberg, Beymer, Anderson, & Schmidt, 2018). For model parameterization, variances were assumed to be equal, and covariances were fixed at zero. A series of LPA models were run, beginning with 32 one profile and successively adding more profiles. The optimal number of latent profiles was determined using the Bayesian information criterion (BIC) and the Bootstrapped likelihood-ratio test (BLRT). Lower BIC values indicate a better model. Specifically, a difference of 10 points between BIC values has been said to provide âvery strongâ evidence (i.e., an odds ratio of 150:1) that the model with the lower value is the better fitting model (Raftery, 1995, p.139). The BLRT is a significance test used to determine whether a model with k-1 profiles should be rejected in favour of the model with more (i.e., k) profiles. Both the BIC and the BLRT have been suggested to be the best indicators to correctly identify the number of classes (Nylund, Asparouhov, & MutheÌn, 2007). Entropy was also examined, which is a measure of the level of separation among profiles (Tein, Coxe, & Cham, 2013). Entropy values range from 0 to 1, with values greater than .80, indicating high discrimination among profiles (Lubke & Muthén, 2007). Additionally, the class profiles were visually examined to evaluate the interpretability of the model and identify the distinguishing features of the classes. 2.6 Results 2.6.1 Descriptive Statistics 2.6.1.1 Prosocial Behaviour Task. Descriptive statistics for helping behaviour in the prosocial task are presented in Table 1. To confirm that the patientsâ level of distress was used to inform the participantsâ help ratings in the prosocial behaviour task, a dependent samples t-test was used to compare help ratings to pain and neutral stimuli. Indeed, participants chose to offer significantly more help (i.e., time) to patients displaying pain expressions (M = 46.33, SD = 19.11) than neutral expressions (M = 12.83, SD = 14.61), t(282) = 31.77, p < .001, Mdifference = 33.50, 95% CI [31.42, 35.57]. Next, a mixed model 33 ANOVA was conducted to examine the effect of patient gender and participant gender on help ratings. Significant main effects for patient gender, F(1, 281) = 445.71, p < .001, Mdifference = 13.29, and participant gender, F(1, 281) = 5.85, p = .016, Mdifference = 5.06, were found, as well as a significant interaction effect, F(1, 281) = 5.02, p = .026. Specifically, female patients (M = 48.19, SD = 20.22) were offered significantly more help than male patients (M = 34.90, SD = 16.20), particularly by female participants. Despite the interaction effect, for the purpose of this study, help ratings were collapsed across patient and participant gender for all analyses. 34 Table 1 Descriptive Information for Prosocial Behaviour Task Stimuli Gender N Minimum Maximum Mean SD Pain Female 180 0.14 100.00 48.17 18.48 Male 103 0.00 94.21 43.10 19.84 Total 283 0.00 100.00 46.33 19.11 Pain-Female Female 180 0.14 100.00 50.58 19.50 Male 103 0.00 96.89 44.00 20.85 Total 283 0.00 100.00 48.19 20.22 Pain-Male Female 180 0.10 89.00 36.29 15.71 Male 103 0.00 82.40 32.46 16.34 Total 283 0.00 89.00 34.90 16.20 Neutral Female 180 0.00 76.83 14.18 15.37 Male 103 0.00 64.67 10.46 12.92 Total 283 0.00 76.83 12.83 14.61 Neutral-Female Female 180 0.00 90.00 14.17 17.19 Male 103 0.00 64.67 11.18 15.29 Total 283 0.00 90.00 13.08 15.17 Neutral-Male Female 180 0.00 80.00 14.20 16.18 Male 103 0.00 72.00 9.75 12.83 Total 283 0.00 80.00 12.58 15.17 Note. Descriptive statistics refer to helping behaviour in the prosocial behaviour task, broken down by stimuli and participant gender. Helping behaviour was measured from 0% (no help) to 100% (maximum time allocated). 2.6.1.2 Predictor Variables. Descriptive statistics for the predictor variables, as well as their bivariate correlation with helping behaviour, are presented in Table 2. As shown, help ratings for pain stimuli were significantly and positively associated with Empathic Concern, Perspective Taking, Sensitivity, and Response Bias; significant negative associations were found for Self-Centered Impulsivity and Coldheartedness. The strongest 35 relationship found was between helping behaviour and Coldheartedness, r(283) = -.244, p < .001. Table 2 Descriptive Information for Predictor Variables and Correlation with Helping Behaviour Variable Minimum Maximum Mean SD Help-Pain r IRI-EC 7.00 35.00 26.53 4.98 .160** IRI-PT 10.00 35.00 25.71 4.93 .171** IRI-PD 9.00 35.00 19.23 4.62 .041 IRI-FA 9.00 35.00 24.70 5.39 .076 ERQ-CR 9.00 42.00 29.62 6.04 .057 ERQ-ES 4.00 27.00 15.02 4.94 .001 PPI-FD 21.00 53.00 37.81 6.61 -.090 PPI-SCI 23.00 65.00 40.33 7.63 -.234** PPI-CH 5.00 20.00 9.64 2.82 -.244** Sensitivity 0.39 0.81 0.62 0.08 .206** Response Bias 0.50 6.56 0.47 1.08 .219** Note. Help-Pain r = Pearson correlations between predictor variables and helping behaviour to pain stimuli. IRI-EC = Empathic Concern; IRI-PT = Perspective Taking; IRI-PD = Personal Distress; IRI-FA = Fantasy; ERQ-CR = Cognitive Reappraisal; ERQ-ES = Expressive Suppression; PPI-FD = Fearless Dominance; PPI-SCI = Self-Centered Impulsivity; PPI-CH = Coldheartedness; Sensitivity = ability to discriminate facial expressions of pain; Response Bias = propensity to infer pain in others. N = 283. ** p < .01. 2.6.2 Research Question 1: Empathy and Prosocial Behaviour Next, multiple regression was conducted to compare the four empathy subscales as predictors of helping behaviour in the prosocial behaviour task. Results revealed that the overall model was significant, F(4, 278) = 2.73, MSE = 356.41, p = .029, R2 = .038, with empathy subscales explaining approximately 3.8% of the variance in helping behaviour towards patients in pain. It was originally hypothesized that Personal Distress would be the strongest predictor of helping behaviour. However, contrary to what was hypothesized, and 36 despite significant bivariate correlations with Empathic Concern and Personal Distress, none of the subscales emerged as significant individual predictors (all ps > .05). Although Personal Distress did not emerge as a significant linear predictor of helping behaviour in the multiple regression model, it is possible that a significant linear or curvilinear relationship exists when examined independently. As such, multiple regression with linear and quadratic components was used be used to explore the predictive influence of Personal Distress on helping behaviour. It was hypothesized that Personal Distress would have a significant, positive coefficient for the quadratic term, indicating a curvilinear relationship. This hypothesis was not supported; both the linear, b =-1.35, t(280) = 0.85, p = .39, and quadratic terms, b = 0.04, t(280) = 0.98, p = .33, were nonsignificant, F(2, 280) = 0.72, p = .49, R2 = .005. 2.6.3 Research Question 2: Emotional Response Profiles LPA was then used to explore response patterns across six indicator variables: Empathic Concern, Personal Distress, Cognitive Reappraisal, Self-Centered Impulsivity, Coldheartedness, and Sensitivity. Bivariate correlations among the indicator variables are displayed in Table 3. A total of four LPA models were examined, ranging from one to four profile solutions. The model fit indices for these solutions are presented in Table 4. The three-profile solution had the lowest BIC and highest entropy values. Moreover, the BLRT p-value comparing the three-profile and four-profile model was nonsignificant (p = .06), indicating that the four-profile model should be rejected in favour of the three-profile model. On the basis of these fit indices, the three-profile model was selected as the best fitting model. 37 Table 3 Bivariate Pearson Correlations Among Indicator Variables Variable IRI-EC IRI-PD ERQ-CR PPI-SCI PPI-CH Sensitivity IRI-EC 1.00 IRI-PD .10 1.00 ERQ-CR .18** -.18** 1.00 PPI-SCI -.35** .11 -.18** 1.00 PPI-CH -.71** -.12* -.10 .31** 1.00 Sensitivity .06 -.07 .09 -.21** -.06** 1.00 Note. IRI-EC = Empathic Concern; IRI-PD = Personal Distress; ERQ-CR = Cognitive Reappraisal; PPI-SCI = Self-Centered Impulsivity; PPI-CH = Coldheartedness; Sensitivity = ability to discriminate facial expressions of pain; Response Bias = propensity to infer pain in others. N = 283. * p < .05, ** p < .01. Table 4 Fit Indices for Latent Profiles Defined by Emotional Response Variables Profiles BIC Entropy BLRT 1 7994.29 â â 2 7842.65 0.88 0.01 3 7816.65 0.88 0.01 4 7834.97 0.70 0.06 Note. Bolded values indicate the best model fit that was chosen. Figure 2 displays the mean standardized scores obtained on the indicator variables for each of the three profiles. The unstandardized means are presented in Table 5. Profile 1 (n = 111) was characterized by scores above the mean for Empathic Concern, Cognitive Reappraisal, and Sensitivity, scores below the mean for Self-Centered Impulsivity and Coldheartedness, and average levels of Personal Distress. This profile was labelled âSensitive Respondersâ. Profile 2 (n = 131) was characterized by scores above the mean for Personal Distress, Self-Centered Impulsivity and Coldheartedness, and average to low scores on Empathic Concern, Cognitive Reappraisal, and Sensitivity. This profile was labelled 38 âIndifferent Respondersâ. Similarly, Profile 3 (n = 41) was characterized by scores above the mean for Self-Centered Impulsivity and Coldheartedness, and average to low scores on Cognitive Reappraisal and Sensitivity. However, unlike Profile 2, it was associated with below average scores for Empathic Concern and Personal Distress. This profile was labelled âInsensitive Respondersâ. Figure 2 Profile Means Across Indicator Variables Note. Scores on indicator variables have been standardized. IRI-EC = Empathic Concern; IRI-PD = Personal Distress; ERQ-CR = Cognitive Reappraisal; PPI-SCI = Self-Centered Impulsivity; PPI-CH = Coldheartedness; Sensitivity = ability to discriminate facial expressions of pain. N = 283. 39 Table 5 Profile Means on Each Indicator Variable Indicator Variables Sensitive Responders Indifferent Responders Insensitive Responders n = 111 n = 131 n = 41 IRI-EC 30.35 25.69 18.36 IRI-PD 19.03 19.96 17.52 ERQ-CR 31.12 28.86 27.77 PPI-SCI 36.34 42.66 44.26 PPI-CH 7.32 10.38 13.93 Sensitivity 0.63 0.61 0.60 Note. IRI-EC = Empathic Concern; IRI-PD = Personal Distress; ERQ-CR = Cognitive Reappraisal; PPI-SCI = Self-Centered Impulsivity; PPI-CH = Coldheartedness; Sensitivity = ability to discriminate facial expressions of pain. N = 283. To further elucidate the profiles, independent ANOVAs were then conducted comparing the three groups on external predictor variables not included in the LPA (i.e., Perspective Taking, Fantasy, Expressive Suppression, Fearless Dominance, and Response Bias). The results are presented in Table 6. As shown, the latent profiles significantly differed in Perspective Taking, Fantasy, and Expressive Suppression. Specifically, all three profiles differed in their levels of Perspective Taking and Fantasy, with Profile 1 (Sensitive Responders) characterized by the highest levels, followed by Profile 2 (Indifferent Responders) and Profile 3 (Insensitive Responders), respectively. In contrast, individuals in Profile 2 were significantly higher in Expressive Suppression than those in Profile 3. These differences among the profiles in empathy and emotional regulation are in line with the profile characterization from the LPA. No significant differences were found among the profiles on Fearless Dominance or Response Bias. 40 Table 6 ANOVA Comparison with External Variables Across Profiles External Variables Sensitive Responders M (SD) Indifferent Responders M (SD) Insensitive Responders M (SD) F η2 IRI-PT 28.52 (3.92) 24.76 (3.96) 21.12 (5.65) 51.96***a,b,c .270 IRI-FA 26.61 (5.40) 24.27 (4.26) 20.85 (6.27) 20.32*** a,b,c .127 ERQ-ES 14.10 (5.25) 15.66 (4.65) 15.44 (4.69) 3.24*a .023 PPI-FD 37.62 (6.83) 37.49 (6.23) 39.37 (7.14) 1.34 .009 Response Bias 1.62 (1.25) 1.33 (0.83) 1.52 (1.23) 2.20 .015 Note. Posthoc comparisons with Bonferroni adjustments were conducted following significant results. IRI-PT = Perspective Taking; IRI-FA = Fantasy; ERQ-ES = Expression Suppression; PPI-FD = Fearless Dominance; Response Bias = propensity to infer pain in others. N = 283. a Posthoc tests indicated significant differences between Class 1 and Class 2. b Posthoc tests indicated significant differences between Class 1 and Class 3. c Posthoc tests indicated significant differences between Class 2 and Class 3. * p-value < .05. ** p-value < .01. *** p-value < .001. Next, an independent ANOVA was conducted to determine whether help ratings differed among the three the profiles. Significant differences in help offered were found among the three profiles, F(2, 280) = 7.24, MSE = 349.70, p < .001, η2 = .05, representing a moderately sized effect by Cohenâs conventions. To further investigate this effect, follow-up pairwise comparisons using the Bonferroni adjustment were conducted. Results revealed that Sensitive Responders (M = 51.52, SD = 19.10) offered significantly more help to patients in pain when compared to both Indifferent Responders (M = 43.47, SD = 17.62, p = .003) and Insensitive Responders (M = 41.38, SD = 20.89, p = .01). Help ratings, however, did not significantly differ between Indifferent Responders and Insensitive Responders, p > .05. 2.7 Summary This study aimed to clarify the relationship between empathy and prosocial behaviour towards patients in pain, using a sample of undergraduate students. Specifically, the two 41 primary goals were to investigate the possibility of a curvilinear relationship between personal distress and helping behaviour, and to examine the potential moderating role of emotional processing characteristics using latent class analysis. It was hypothesized that participantsâ level of personal distress would be a significant predictor of prosocial behaviour, and that this relationship would be curvilinear in nature. Secondly, it was hypothesized that empathy, third-person pain perception, emotion regulation, and psychopathic traits would interact to form emotional response profiles that would distinguish engagement in helping behaviour. Overall, findings from this study did not support the first hypothesis but were in line the second hypothesis. 2.7.1 Prosocial Behaviour Task As would be expected, participants were found to offer significantly more help to patients displaying painful, as opposed to neutral, facial expressions. Moreover, a significant interaction emerged between patient and participant gender, with the most help being offered to female patients, particularly by female participants. Although not a primary focus of this dissertation, these findings are interesting to consider and add to a large, and somewhat conflicting, body of literature investigating the influence of patient and provider gender on the perception and treatment of pain (e.g., Bartley et al., 2015; Coll, Budell, Rainville, Decety, & Jackson, 2012; Gleichgerrcht & Decety, 2014; Hoffmann & Tarzian, 2001; Safdar et al., 2009; Wandner et al., 2014). For example, Robinson and Wise (2003) found that when participants were shown videos of individuals undergoing the cold-pressor task, female videos were rated as having more pain and female participants were found to rate pain intensity significantly higher than male participants. Conversely, in a study where healthcare providers were asked to rate the 42 intensity of painful expressions using virtual human patients, male patientsâ pain was rated as being more intense than female patientsâ pain (Wandner et al., 2014). Regarding the treatment of pain, findings generally suggest that female patients are at a disadvantage. Specifically, it has been reported that female patients are less likely to receive analgesics than their male counterparts when presenting with pain (e.g., Chen et al., 2008; Michael, Sporer, & Youngblood, 2007). The findings from the present study may initially appear inconsistent with the aforementioned research, as females were found to receive more help than males. It should be noted, however, that âhelpâ in this study was operationalized as time spent with a patient, as opposed to the administration of pain medication. It may be that while females are less likely to receive pain treatment focused on relieving the physical discomfort of pain, they may be more likely to receive treatment focused on relieving emotional discomfort. Indeed, other research suggests that female patients are more likely to receive antidepressant mediations and mental health referrals in response to their pain (e.g., Bernardes et al., 2013). Cumulatively, these findings highlight that important role patient and provider gender may play in the treatment of pain. It is important to note that because participants were asked about their gender, biological sex differences were not explored, and results should be interpreted as such. 2.7.2 Research Question 1: Empathy and Prosocial Behaviour To address the first research question, heterogeneity in helping behavior during the prosocial task was explored using the four empathy components of the IRI as predictors. Based on the study by Coll et al. (2017), it was hypothesized that personal distress would emerge as the strongest predictor, with lower levels of personal distress associated with higher levels of helping behaviour towards patients in pain. Contrary to expectation, none of 43 the subscales emerged as significant predictors, and together the components of empathy explained less than 4% of the variability in helping behaviour. Although a relationship between personal distress and prosocial behaviour was anticipated, the literature to date has been mixed with some studies reporting positive associations (e.g., Hein et al., 2011) and others reporting negative associations (e.g., Coll et al., 2017). One potential explanation for these conflicting findings is that the relationship is curvilinear, with both high and low levels of personal distress increasing the likelihood of helping behaviour, thus decreasing the ability to detect linear correlations. This hypothesis, however, was not supported. No significant relationship, linear nor curvilinear, was found between personal distress and helping behaviour. 2.7.3 Research Question 2: Emotional Response Profiles The conflicting findings in the literature regarding the effect of empathy on prosocial behaviour may also point to the presence of moderating variables. Specifically, the traits of interest were ones pertaining to emotional processing that have previously been found to influence levels of helping behaviour: third-person pain sensitivity, cognitive reappraisal, self-centered impulsivity, and coldheartedness. Latent profile analysis was used to examine the interactions among these variables and empathy, and the influence of these interactions on prosocial behaviour. It was first hypothesized that these variables would interact such that groups of similar response patterns would emerge, representing âemotional response profilesâ. It was also hypothesized that these profiles would differentially predict willingness to help patients in pain. Results supported both hypotheses. Three distinct profiles of emotional responding were empirically detected: Sensitive Responders, Indifferent Responders, and Insensitive 44 Responders. Sensitive Responders were characterized by the highest levels of empathic concern, cognitive reappraisal, and pain sensitivity, moderate levels of personal distress, and the lowest levels of self-centered impulsivity and coldheartedness. Indifferent Responders were found to have the highest level of personal distress, with moderate levels of the other indicator variables. Insensitive Responders were lowest in empathic concern, personal distress, cognitive reappraisal, and pain sensitivity; they were highest in self-centered impulsivity and personal distress. Follow-up analyses also revealed that groups differed in level of perspective taking, fantasy, and expressive suppression. Importantly, the three profiles were found to significantly differ in level of help offered during the prosocial behaviour task â with Sensitive Responders offering significantly more help than both Indifferent and Insensitive Responders. It should be noted, however, that Indifferent Responders and Insensitive Responders did not significantly differ in helping behaviour. In summary, Study 1 found that personal distress was not meaningfully related with willingness to engage in helping behaviour towards patients in pain, when considered alone using variable-centered approaches. Results from the LPA, however, provide preliminary evidence that personal distress may interact with other personality characteristics to form emotional-response profiles, and that these profiles may in turn influence helping behaviour. Because LPA is exploratory in nature, an important next step is to determine whether these profiles can be replicated in a separate group of students (e.g., Marsh, Lüdtke, Trautwein & Morin, 2009; Taku & McLarnon, 2018). Moreover, it is important to determine whether the profiles identified, and their predictive relationship with helping behaviour, extend to healthcare professionals. As such, a primary aim of Study 2 was to assess the replicability of these findings. 45 CHAPTER 3 Study 2 3.1 Overview of Study 2 Study 2 was a replication with modification of Study 1. Specifically, Study 2 introduced a remuneration condition in order to examine the influence of personal cost on prosocial behaviour. Additionally, the participant sample of Study 2 included both undergraduate students and medical residents to determine whether the profiles identified in Study 1 would replicate and extend to healthcare professionals. It was hypothesized that the relationship between empathy and prosocial behaviour would be modified by the method of remuneration. More specifically, it was anticipated that when the personal cost of helping is low (i.e., salary condition), personal distress levels would emerge as the strongest predictor of willingness to help. When the personal cost of helping is high (i.e., capitation), however, it was hypothesized that empathic concern would be most predictive of helping. Secondly, it was hypothesized that the moderating relationships found in Study 1 would be replicated. Specifically, it was hypothesized that the emotional response profiles identified in Study 1 would also emerge in Study 2. Lastly, the ability to predict engagement in prosocial behaviour was compared among the emotional response profiles, personal cost of helping, and participant group (i.e., student or medical resident) in an exploratory fashion. 3.2 Participants Participants for this study were taken from two samples: undergraduate students and medical residents. Specifically, 209 undergraduate students (89 women, 119 men, and 1 nonbinary) at the UBCO Campus were recruited using the Department of Psychologyâs SONA online research system. The majority of participants (69.9%) were between the ages 46 of 17-20 years old. Similar to Study 1, participants primarily self-identified as White (55%), followed by Asian (26.8%), Other (10%), Black (5.3%), and Latino/Latina (2.9%). Additionally, 69 medical residents (44 women, 23 men, and 2 prefer not to say) enrolled with the UBC MD program were recruited. All participants were completing residency training in Family Medicine, with half in their first year (50.7%) and half in their second year (49.3%). The majority of participants (62.1%) were between the ages of 26-30 years old. Again, participants primarily self-identified as White (56.5%), followed by Asian (27.5%), Other (14.5%), and Latino/Latina (1.5%). To recruit participants, an invitation email detailing the study (with a direct hyperlink to the Qualtrics survey) was sent to medical residents by the UBC Department of Family Medicine. All participants were randomly assigned to one of two incentive conditions: salary (i.e., time-based) or capitation (i.e., patient-based). In exchange for their involvement, both undergraduate and medical student participants were awarded a $20.00 monetary incentive. Inclusion and exclusion criteria were the same as that in Study 1: normal or corrected vision, fluent in reading English, and no reported history of a painful disorder. 3.3 Materials Stimuli presentation and recording of responses was carried out using Qualtrics (Qualtrics, Provo, UT). Undergraduate participants were given option of completing the study online (n = 108) or in-person (n = 101); all medical residents completed the study online. 3.3.1 Assessment of Prosocial Behavior The prosocial behavior task described in Study 1 was used with modification to allow for the examination of monetary incentive on helping behavior. During this task, participants 47 completed the same experimental trials from Study 1; however, participants were told that they have a maximum of 7 minutes to complete the task. Participants who chose to offer little to no help would be able to successfully complete all 20 trials during the time-period; however, the number of patients a participant could see decreased as the amount of help offered increased. As such, the decision to offer help to patients was (ostensibly) at a personal cost (e.g., paid less) for participants in the capitation-based condition, who were informed that their level of compensation was dependent on number of patients seen during the 7-minute task. The 20 experimental trials were presented in the same fixed-order for all participants. 3.3.2 Assessment of Third-Person Pain The third-person pain task, previously described in Study 1, was used to assess perceptual sensitivity and response bias to othersâ pain. 3.3.3 Self-Report Measures All self-report measures used in Study 1 were used in Study 2. Specifically, participants completed a demographics questionnaire, IRI, ERQ, and PPI-R-40. 3.4 Procedure Undergraduate student and medical residents completed the study via a Qualtrics link. Upon opening the hyperlink, participants were presented with a consent form to which they had to agree to proceed with the study. Upon providing consent, participants (unbeknownst to them) were randomly assigned into one of two incentive conditions: the salary condition or the capitation condition. Participants were then directed to the prosocial behaviour task, followed by the third-person pain perception task and self-report measures (i.e., IRI, ERQ, and PPI-R-40). 48 3.4.1 Prosocial Behaviour Task Participants completed the same prosocial behaviour task from Study 1; however, the instructions provided prior to the task were modified. Specifically, participants were asked to place themselves in the role of a healthcare professional in charge of treating pain in patients. They were told that they would have 7 minutes to complete the task and that there were 20 patients waiting to be seen. As in Study 1, participants were informed that they would receive visual feedback showing the effects of the help they provide for each patient and that the patientâs pain would be reduced in proportion to the amount of help chose to offer. Participants in the salary condition were then be told that they would receive $20.00 in exchange for their help, regardless of the number of patients they saw during the task. In contrast, participants in the capitation-based condition were told their compensation would depend on the number of patients treated during the task. Specifically, these participants were told that they would receive $1.00 per patient, earning a maximum of $20.00 if they were able to see all 20 patients during the timeframe. All participants were reminded that offering help to patients would be associated with increased workload and time, thus limiting the number of patients they could see during the task. Participants then completed the third-person pain perception task and self-report measures, as described in Study 1. After completing the study, participants were debriefed and informed that they would each receive $20.00, regardless of the number of patients seen during the prosocial behaviour task. They were then directed to a separate Qualtrics link and instructed to enter their email address and contact information so they could be sent their compensation electronically via email transfer. A separate Qualtrics link was used to ensure that any identifying information was not attached to participantsâ data. 49 3.5 Data Reduction Using the methods described in Study 1, help ratings from the prosocial task were averaged to obtain a helping score for pain and neutral trials. In addition, total number of patients seen was calculated and served as the primary measure of helping behaviour. Similarly, scores for pain sensitivity, response bias, empathy, emotion regulation, and psychopathic traits were calculated as detailed in Study 1. 3.6 Analytic Strategy The analytic approach used in Study 1 was largely replicated, with a few modifications to accommodate the additional group variables (i.e., participant and incentive group) and task changes (i.e., 7-minute time limit). First, a dependent samples t-test was conducted to confirm that participants responded with higher help ratings to pain stimuli than neutral stimuli. Next, descriptive statistics for the prosocial task were explored. A factorial ANOVA was used to compare helping behaviour between participant and incentive groups on two outcome measures: average help ratings and number of patients seen. Descriptive statistics were then calculated for all predictor variables, as well as their bivariate correlations with number of patients seen. Due to the inclusion of these new variables (i.e., participant group, incentive condition, and patients seen), it should be noted that gender effects were not explored here for pragmatic reasons. To address research question 3, multiple regression was then used to compare the predictive role of empathy components in number of patients seen for both the capitation and salary conditions. Next, to test research question 4, the LPA procedures from Study 1 were replicated using the same six indicator variables: Empathic Concern, Personal Distress, Cognitive Reappraisal, and Self-Centered Impulsivity, Coldheartedness, and Sensitivity. 50 Last, factorial ANOVA was used to examine and compare the effects of participant, incentive, and profile group on number of patients seen. 3.7 Results 3.7.1 Descriptive Statistics 3.7.1.1 Help Ratings. As in Study 1, participants overall offered significantly more help to patients displaying pain expressions (M = 49.03, SD = 18.55) than neutral expressions (M = 11.42, SD = 16.64), t(277) = 34.79, p < .001, Mdifference = 37.56, 95% CI [35.43, 30.69], indicating task comprehension and attention to stimuli. Next, a factorial ANOVA was used to determine whether average help ratings differed between the participant groups (i.e., student and resident) and incentive groups (i.e., capitation and salary). Significant main effects were found for both participant group, F(1, 277) = 3.93, MSE = 251.94, p = .048, and incentive group, F(1, 277) = 4.21, MSE = 251.94, p = .041. Specifically, it was found that more help was offered by residents (M = 41.95, SD = 16.69) than students (M = 37.65, SD = 15.73), and more help was offered in the salary condition (M = 51.20, SD = 17.99) than the capitation condition (M = 46.96, SD = 18.90; see Table 7). The interaction, however, was not significant, F(1, 277) = .001, MSE = 251.94, p = .972. 51 Table 7 Descriptive Information for Help Ratings in the Prosocial Behaviour Task Sample Incentive N Minimum Maximum Mean SD Students Capitation 106 0.05 88.33 35.46 16.07 Salary 103 6.95 79.14 39.91 15.13 Total 209 0.05 88.33 37.65 15.73 Residents Capitation 36 0.55 99.00 39.75 16.89 Salary 33 15.19 80.86 44.36 16.39 Total 69 0.55 99.00 41.95 16.69 Note. Descriptive statistics refer to average help ratings in the prosocial behaviour task, broken down by sample and incentive group. Help ratings were averaged across pain and neutral stimuli. The number of ratings contributing to participantsâ average varied depending on the number of patients seen in the 7-minute task. Higher numbers indicate more time was spent with patients, and thus more prosocial help was offered. 3.7.1.2 Patients Seen. As would be expected, the number of patients seen during the task was strongly and inversely related to average help ratings, r(278) = -.758, p < .001. That is, higher help ratings resulted in more time spent on patients, decreasing the overall number of patients seen during the task. It should be noted that these outcome variables were not perfectly correlated as wait time did not increase with every one-unit increase in help ratings. That is, wait time increased in 15-second intervals for every 20-unit increase in help ratings. For example, a help rating of 21% corresponded with a 15-second waiting period and a rating of 41% corresponded with a 30-second waiting period; however, help ratings of 21% and 40% were associated with the same wait time (i.e., 15 seconds). As such, higher overall help ratings did not always result in longer wait times. However, due to the strong correlation between the two variables, and because average help ratings were based on differing numbers of stimuli for each participant, patients seen was used as the sole outcome variable for helping behaviour for the main analyses. 52 Figure 3 displays a frequency distribution for the number of patients seen during the 7-minute task. As shown, the number of patients ranged from 6 to 20. On average, participants viewed just over half (M = 11.20; SD = 3.40) of the patients, with only 5% of participants (n = 14) making it through all 20 patients. Descriptive information for the number of patients seen, broken down by participant and incentive group, is presented in Table 8. Consistent with the help rating findings, residents on average made it through fewer patients than their student counterparts (i.e., residents were more prosocial). Moreover, fewer patients were seen in the salary condition than the capitation condition, although this pattern only emerged in the student group. However, a factorial ANOVA revealed that these differences were not statistically significant (all ps > .05). 53 Figure 3 Frequency Distribution of Helping Behaviour in Prosocial Behaviour Task Note. Helping behaviour was measured as the number of patients (out of 20) seen during the seven-minute prosocial task. Lower numbers indicate more time was spent with patients, and thus more prosocial help was offered. N = 278. 54 Table 8 Descriptive Information for Number of Patients Seen in the Prosocial Behaviour Task Sample Incentive N Minimum Maximum Mean SD Students Capitation 106 6.00 20.00 11.58 3.31 Salary 103 7.00 20.00 10.91 3.25 Total 209 6.00 20.00 11.25 3.29 Residents Capitation 36 6.00 20.00 10.72 3.14 Salary 33 7.00 20.00 11.39 4.32 Total 69 6.00 20.00 11.04 3.74 Note. Descriptive statistics refer to helping behaviour in the prosocial behaviour task, broken down by sample and incentive group. Helping behaviour was measured as the number of patients seen during the seven-minute prosocial task. Lower numbers indicate more time was spent with patients, and thus more prosocial help was offered. 3.7.1.3 Predictor Variables. Descriptive statistics for the predictor variables are presented in Table 9. As in the prosocial task, students and residents exhibited similar scores to one another across the emotional responding variables. The only significant difference that emerged was for Self-Centered Impulsivity, with residents (M = 34.94, SD = 6.94) scoring lower than the student sample (M = 42.49, SD = 7.16). Because of the similarity on predictor variables and helping behaviour, as well as the small sample size for residents, helping behaviour was collapsed across participant groups for the remaining analyses. That is, the student group (n = 209) and resident group (n = 69) were combined and treated as a single sample (N = 278). 55 Table 9 Descriptive Information for Predictor Variables Compared Between Participant Groups Variable Student n = 209 Resident n = 69 t p Mean SD Mean SD IRI-EC 26.60 4.77 27.90 4.27 1.96 .051 IRI-PT 25.62 4.83 26.52 4.25 1.39 .166 IRI-PD 18.62 4.79 18.01 4.09 0.94 .349 IRI-FA 25.18 5.21 23.28 5.29 2.62 .009 ERQ-CR 30.42 6.09 30.67 6.01 0.30 .767 ERQ-ES 16.33 5.21 14.32 5.18 2.78 .006 PPI-FD 38.86 7.14 37.35 5.49 1.83a .069 PPI-SCI 42.49 7.16 34.94 6.94 7.65 <.001* PPI-CH 9.75 2.52 9.72 2.25 0.08 .938 Sensitivity 0.62 0.08 0.64 0.09 1.46 .144 Response Bias 1.56 1.15 1.69 1.09 0.82 .415 Note. IRI-EC = Empathic Concern; IRI-PT = Perspective Taking; IRI-PD = Personal Distress; IRI-FA = Fantasy; ERQ-CR = Cognitive Reappraisal; ERQ-ES = Expressive Suppression; PPI-FD = Fearless Dominance; PPI-SCI = Self-Centered Impulsivity; PPI-CH = Coldheartedness; Sensitivity = ability to discriminate facial expressions of pain; Response Bias = propensity to infer pain in others. a Welch t-test statistic reported as homogeneity of variances violated *p < .004. Bonferroni correction (αaltered = .05/11) = .004. As in Study 1, bivariate correlations were conducted to explore the relationships between helping behaviour and predictor variables (see Table 10). Correlation analyses were performed separately for the capitation and salary conditions. In the capitation condition, significant negative correlations were found for Empathic Concern, Fantasy, Sensitivity, and Response Bias, and significant positive correlations were found for Expressive Suppression and Coldheartedness. The same pattern emerged for the salary condition except that Fantasy was not significantly associated with helping behaviour. These correlations are similar to those observed in Study 1; however, in the present study, Perspective Taking and Self- 56 Centered Impulsivity were not significantly associated with helping behaviour, and instead, Expressive Suppression and Fantasy were found to be significant. Table 10 Pearsonâs Correlations Between Predictor Variables and Helping Behaviour Variable Incentive Group Capitation (n = 142) Salary (n = 136) IRI-EC -.200** -.178* IRI-PT -.092 -.004 IRI-PD -.089 -.070 IRI-FA -.282** -.031 ERQ-CR .005 -.064 ERQ-ES .189* .221** PPI-FD -.058 .090 PPI-SCI -.019 .075 PPI-CH .173* .152* Sensitivity -.255** -.241** Response Bias -.246** -.174* Note. Pearson correlations are reported between predictor variables and the number of patients seen during the seven-minute task. Negative correlations indicate more prosocial behaviour offered to patients as trait increases. IRI-EC = Empathic Concern; IRI-PT = Perspective Taking; IRI-PD = Personal Distress; IRI-FA = Fantasy; ERQ-CR = Cognitive Reappraisal; ERQ-ES = Expressive Suppression; PPI-FD = Fearless Dominance; PPI-SCI = Self-Centered Impulsivity; PPI-CH = Coldheartedness; Sensitivity = ability to discriminate facial expressions of pain; Response Bias = propensity to infer pain in others. * p < .05. ** p < .01. 3.7.2 Research Question 3: Personal Cost of Helping Next, two separate multiple regression analyses were used to determine whether the relationship between empathy and prosocial behaviour differed, depending on the personal cost of helping (i.e., incentive group). As previously mentioned, it was hypothesized that Personal Distress would emerge as the strongest predictor of helping behaviour in the salary 57 condition and that Empathic Concern would emerge as the strongest predictor in the capitation conditions. In the salary condition, the linear combination of the four empathy subscales did not significantly predict helping behaviour, F(4, 131) = 1.65, MSE = 12.19, p = .165, R2 = .048. Although this null finding was not anticipated, it is not surprising given the small bivariate correlation with only Empathic Concern. In the capitation condition, results revealed that the overall model was significant, F(4, 137) = 3.46, MSE = 10.06, p = .010, R2 = .092, with empathy subscales explaining approximately 9.2% of the variability in helping behaviour. Contrary to what was hypothesized, and despite a significant bivariate correlation with Empathic Concern, only Fantasy emerged as a significant individual predictor, t(137) = 2.70, p = .008. 3.7.3 Research Question 4: Generalizability of Emotional Response Profiles The LPA procedures from Study 1 were then repeated using the same six indicator variables: Empathic Concern, Personal Distress, Cognitive Reappraisal, Self-Centered Impulsivity, Coldheartedness, and Sensitivity. The bivariate correlations among indicator variables are displayed in Table 11. The model fit indices for the solutions are presented in Table 12. As in Study 1, the three-profile solution had the lowest BIC and highest entropy values. Moreover, the BLRT p-value comparing the three-profile and four-profile model was nonsignificant (p = .48), indicating that the three-profile model offered a better fit. Based on these indices, the three-profile model was selected as the best fitting model. 58 Table 11 Bivariate Pearson Correlations Among Indicator Variables Variable IRI-EC IRI-PD ERQ-CR PPI-SCI PPI-CH Sensitivity IRI-EC 1.00 IRI-PD .10 1.00 ERQ-CR .14* -.10 1.00 PPI-SCI -.25** .10 -.13* 1.00 PPI-CH -.58** -.19** -.09 .11 1.00 Sensitivity .12* -.02 .00 -.16** -.11** 1.00 Note. IRI-EC = Empathic Concern; IRI-PD = Personal Distress; ERQ-CR = Cognitive Reappraisal; PPI-SCI = Self-Centered Impulsivity; PPI-CH = Coldheartedness; Sensitivity = ability to discriminate facial expressions of pain; Response Bias = propensity to infer pain in others. N = 278. * p < .05, ** p < .01. Table 12 Fit Indices for Latent Profiles Defined by Emotional Response Variables Profiles BIC Entropy BLRT 1 7759.43 â â 2 7656.83 0.76 .01 3 7641.54 0.79 .01 4 7671.58 0.62 .48 Note. Bolded values indicate the best model fit that was chosen. The overall structure of the emotional response profiles found in Study 1 was replicated, as shown in Figure 4 and Table 13. Given the similarity of the profile solutions, the same naming conventions used in Study 1 were applied. It should be noted, however, in the present study individuals in Profile 3 were characterized by lower levels of Personal Distress and higher levels of Cognitive Reappraisal than in Study 1. Additionally, the profile proportions differed slightly; Profile 1 (Sensitive Responders) comprised the majority of the sample (n = 163), followed by Profile 2 (Indifferent Responders; n = 92) and Profile 3 (Insensitive Responders; n = 23). 59 Figure 4 Profile Means Across Indicator Variables Note. Scores on indicator variables have been standardized. IRI-EC = Empathic Concern; IRI-PD = Personal Distress; ERQ-CR = Cognitive Reappraisal; PPI-SCI = Self-Centered Impulsivity; PPI-CH = Coldheartedness; Sensitivity = ability to discriminate facial expressions of pain. N = 278. 60 Table 13 Profile means on each indicator variable Indicator Variables Sensitive Responders Indifferent Responders Insensitive Responders n = 163 n = 92 n = 23 IRI-EC 29.93 23.91 18.00 IRI-PD 18.50 19.37 14.75 ERQ-CR 31.61 28.37 30.93 PPI-SCI 38.74 42.97 44.28 PPI-CH 8.57 10.69 14.05 Sensitivity 0.64 0.61 0.60 Note. IRI-EC = Empathic Concern, IRI-PD = Personal Distress, ERQ-CR = Cognitive Reappraisal, PPI-SCI = Self-Centered Impulsivity, PPI-CH = Coldheartedness; Sensitivity = ability to discriminate facial expressions of pain. N = 278. Next, the profiles were compared on the external predictor variables not included in the LPA (i.e., Perspective Taking, Fantasy, Expressive Suppression, Fearless Dominance, and Response Bias). The results are presented in Table 14. Consistent with Study 1, the latent profiles significantly differed in Perspective Taking, Fantasy, and Expressive Suppression. Specifically, all three profiles differed in their level of perspectivity taking, with Sensitive Responders characterized by the highest levels, followed by Indifferent Responders and Insensitive Responders, respectively. The same pattern occurred for Fantasy; however, only Sensitive and Indifferent Responders were found to differ significantly. In contrast to Study 1, Insensitive Responders were significantly higher in Expressive Suppression than Indifferent Responders. No significant differences were found among the profiles on Fearless Dominance or Response Bias. Lastly, two chi-square tests were conducted used to examine the effect of participant group and incentive group on profile membership. No significant association was found between profile membership and participant group, Ï2(2) = 3.97, p = .14, or incentive 61 condition, Ï2(2) = 2.54, p = .28. In other words, the frequencies of the emotional response profiles did not differ across levels of participant or incentive group. Table 14 ANOVA Comparison with External Variables Across Profiles External Variables Sensitive Responders M (SD) Indifferent Responders M (SD) Insensitive Responders M (SD) F η2 IRI-PT 27.81 (3.82) 23.57 (3.96) 20.93 (5.48) 51.16***a,b,c .271 IRI-FA 25.52 (5.19) 23.67 (5.02) 23.09 (6.14) 4.90** a .034 ERQ-ES 14.98 (5.53) 16.37 (4.61) 19.70 (3.71) 9.36***b,c .063 PPI-FD 38.44 (6.71) 37.97 (6.90) 40.91 (6.77) 1.75 .013 Response Bias 1.59 (1.34) 1.66 (1.22) 1.27 (0.78) 1.07 .008 Note. Results reported are for five independent ANOVAs with the latent profiles as the independent variable. Posthoc comparisons with the Bonferroni adjustment were conducted following significant results. IRI-PT = Perspective Taking; IRI-FA = Fantasy; ERQ-ES = Expression Suppression; PPI-FD = Fearless Dominance; Response Bias = propensity to infer pain in others. N = 278. a Posthoc test indicated significant differences between Profile 1 and Profile 2. b Posthoc tests indicated significant differences between Profile 1 and Profile 3. c Posthoc tests indicated significant differences between Profile 2 and Profile 3. * p-value < .05. ** p-value < .01. *** p-value < .001. 3.7.4 Research Question 5: Comparing Predictors Next, a 2 Ã 2 Ã 3 factorial ANOVA was conducted to investigate and compare the effects of participant, incentive, and profile group on number of patients seen. As shown in Table 15, there was a main effect of profile group on number of patients seen, F(2, 266) = 4.66, MSE = 11.05, p = .010, η2 = .010. Specifically, follow-up pairwise comparisons using the Bonferroni adjustment revealed that participants belonging to Insensitive Responders (M = 13.30, SD = 3.69) saw significantly more patients and thus offered less help during the task than participants belonging to Sensitive Responders (M = 10.82, SD = 3.27, p = .038) or Indifferent Responders (M = 11.35, SD = 3.39, p = .003). The number of patients seen, however, did not significantly differ between Sensitive Responders and Indifferent 62 Responders, p = .687. No other main effects or interaction effects were significant (all ps > .05). Table 15 Factorial ANOVA Results for Number of Patients Seen Group Variables SS df F p η2 Participanta 13.60 1 0.48 .487 .001 Incentiveb 5.30 1 1.23 .269 .001 Profilesc 103.00 2 4.66 .010** .010 Participant x Incentive 0.50 1 0.04 .836 .000 Participant x Profile 8.20 2 0.37 .692 .001 Incentive x Profile 22.40 2 1.01 .364 .002 Participant x Incentive x Profile 41.30 2 1.87 .157 .004 Residual 2940.20 266 a Participant levels = Residents and Students. b Incentive levels = Capitation and Salary. c Profile levels = Sensitive Responders, Indifferent Responders, and Insensitive Responders. ** p-value < .01. 3.8 Summary The overall aim of this study was to explore and compare the moderating influences of individual (i.e., emotional processing traits) and contextual (i.e., personal cost of helping) factors in the relationship between personal distress and prosocial behaviour towards patients in pain. Specifically, this study introduced a remuneration condition (i.e., salary vs capitation) to manipulate the personal cost of helping. Additionally, to cross-validate the emotional response profiles detected in Study 1, this study was conducted using a different sample of undergraduate students and included a sample of medical residents. Lastly, the influence of emotional response profiles, personal cost of helping, and participant sample on prosocial helping behaviour were examined and compared in an exploratory fashion. It was hypothesized that when the personal cost of helping was low (i.e., salary condition), personal 63 distress would emerge as the strongest empathy predictor of helping behaviour. In contrast, when the personal cost of helping was high (i.e., capitation condition), it was hypothesized that empathic concern would emerge as the strongest predictor. Additionally, it was hypothesized that the structure of the latent profiles detected in Study 1 would be replicated and that these emotional response profiles would predict willingness to engage in helping behaviour. The results of this study were partially in line with the hypotheses. Remuneration was not found to moderate the relationship between personal distress and prosocial behaviour. The structure of the emotional response profiles, however, was generally replicated, and these profiles were found to significantly differ in helping behaviour. 3.8.1. Prosocial Behaviour Task During the 7-minute prosocial behaviour task, the majority of participants viewed just over half of the patients, with only 5% of the participants making it through all 20 patients. Overall, medical residents were found to offer significantly more help to patients in pain than undergraduate students, and as a result, saw significantly fewer patients during the task. This pattern emerged even in the capitation condition, where the decision to offer more help ostensibly came at the personal cost of earning less money. This finding is in line with the study of Coll et al. (2017), who found that healthcare professionals (i.e., nurses and physiotherapists) offered more help to patients in pain than participants without healthcare experience, at the personal cost of increasing overall length of the experiment. Additionally, overall, participants in the salary condition were found to offer significantly more help to patients in pain than participants in the capitation condition, suggesting that the manipulation of personal cost of helping was effective. It should be noted, however, that this significant difference in helping behaviour between incentive conditions did not emerge when using 64 number of patients seen as the outcome variable (as opposed to average help ratings). This discrepancy is likely a result of two factors. First, as noted, higher overall help ratings did not always result in a longer wait-time as wait-time did not increase for every one-unit increase in help ratings. Secondly, this variable had a narrow range of potential values, reducing variability. That is, due to the timing of the task, the fewest number of patients that could be seen was 5, with the highest being 20. Consequently, this restricted range may have reduced the ability to detect significant effects. 3.8.2 Research Question 3: Personal Cost of Helping To examine whether the personal cost of helping moderates the relationship between empathy and helping behaviour, two multiple regression analyses were conducted. It was hypothesized that personal distress would emerge as the strongest predictor in the salary condition, and that empathic concern would emerge as the strongest predictor in the capitation condition. Contrary to expectation, in the salary condition, empathy subscales were not found to significantly predict prosocial behaviour. In the capitation condition, 9.2% of the variability in helping behaviour was explained by the four empathy subscales. However, empathic concern did not emerge as a significant individual predictor, but rather fantasy. It should be noted, however, that when the bivariate correlations were examined, empathic concern was significantly associated with number of patients seen in both the capitation and salary condition, with a slightly stronger correlation in the capitation condition. As in Study 1, personal distress was not significantly associated with helping behaviour in either condition. This result is in contrast to the findings of Coll et al. (2017), who observed that participants were more willing to alleviate patients' pain when low in personal distress. 3.8.3 Research Question 4: Generalizability of Emotional Response Profiles 65 Next, employing the same procedures from Study 1, LPA was conducted to determine whether the emotional response profiles previously derived would replicate in a separate sample of undergraduate students and extend to a sample of healthcare workers. Again, three profiles were empirically detected based on responses across the six indicator variables: personal distress, empathic concern, pain sensitivity, cognitive reappraisal, self-centered impulsivity, and coldheartedness. Importantly, the overall structure of the emotional response profiles found in Study 1 was replicated, with only minor discrepancies. Specifically, Insensitive Responders were characterized by higher levels of cognitive reappraisal and lower levels of personal distress than they were in Study 1. Again, the groups were also found to significantly differ in level of perspective taking, fantasy, and expressive suppression. It should be noted that the profile proportions were slightly different from that of Study 1, with Sensitive Responders now representing the largest group, as opposed to Indifferent Responders. Importantly, it should be noted that medical residents had nearly identical scores as undergraduate students across the predictor variables. Indeed, the only significant difference that emerged was for self-centered impulsivity, with medical residents scoring lower than the student sample. Moreover, profile membership did not vary as a function of participant group. Together, these findings provide initial support for the generalizability of the latent profiles to healthcare providers. 3.8.4 Research Question 5: Comparing Predictors Lastly, a factorial ANOVA was conducted to investigate and compare the effects of participant, incentive, and profile group on number of patients seen. The only significant effect that emerged was for the latent profiles, suggesting that emotional response 66 patterns were a stronger predictor of helping behaviour than participant group (i.e., undergraduate student or medical resident) or incentive condition (i.e., salary or capitation). As in Study 1, Sensitive Responders were more prosocial towards patients in pain (i.e., saw fewer patients) than Insensitive Responders. Interestingly, however, helping behaviour did not differ between Sensitive Responders and Indifferent Responders, despite Sensitive Responders scoring higher in empathic concern and sensitivity, and lower in self-centered impulsivity and coldheartedness. One potential explanation for this finding is that Indifferent Responders may have engaged in helping behaviour but based on different motivations. As previously mentioned, two theories of costly helping have emerged â one pointing to altruistic motivations and the other to egoistic motivations (FeldmanHall et al., 2015). The arousal-reduction hypothesis posits that individuals may be motivated to engage in costly helping due to the desire to alleviate personal discomfort (e.g., Batson, 1987; Hoffman, 1991). In line with this hypothesis, although Indifferent Responders were lower in empathic concern and higher in coldheartedness than Sensitive Responders, they also had the highest levels of personal distress and self-centered impulsivity. In other words, while this group may be less motivated to engage in helping behaviour for altruistic reasons, they may still behave prosocially to relieve their own personal distress. Overall, the findings of this study generally replicate and extend those of Study 1. Again, personal distress was not significantly associated with prosocial helping behaviour, and this relationship was not moderated by the personal cost of helping. However, as in Study 1, evidence was found for the moderating role of emotional response traits. The present study identified three similar emotional response profiles, and again these profiles were differentially related to willingness to help patients in pain. Together, these results 67 provide support for the stability of the emotional response profiles derived in Study 1 and preliminary evidence that they may extend to healthcare professionals. Moreover, these findings suggest that these emotional response profiles play influence prosocial behaviour towards patients in pain. 68 CHAPTER 4 General Discussion Pain has been identified as the most common reason for seeking medical attention in emergency departments, and yet patientsâ pain has been found to be left widely undertreated upon discharge. As previously described, the pervasiveness of these visits, in combination with the undertreatment of pain, can bear long-lasting and detrimental consequences for both the patient and treating healthcare professional (Curtis & Morrell, 2006; Decosterd et al., 2007; Pines & Hollander, 2008; Stalnikowicz, Mahamid, Kaspi, & Brezis, 2005; Todd et al., 2007). Consequently, it is crucial to elucidate factors contributing to the prosocial treatment of pain. One factor commonly associated with prosocial behaviour is personal distress â a component of empathy that relates to oneâs experience of emotional discomfort in response to othersâ suffering (e.g., Cialdini et al., 1987; Coll et al., 2017; Hein et al., 2011). Interestingly, however, different patterns of associations have emerged in literature: one suggesting that high levels of personal distress are associated with increased willingness to help and the other identifying low levels of personal distress as a precipitating factor of helping behaviour (e.g., Coll et al., 2017; Hein et al., 2011). Further adding to the confusion, research findings suggest that the type of affective empathy (i.e., personal distress or empathic concern) associated with prosocial tendency may vary depending on the context in which the help occurs. Specifically, FeldmanHall et al. (2015) found that empathic concern, not personal distress, was predictive of prosocial behaviour when the decision to help came at a personal cost. As such, the current studies aimed to clarify the complex nature of the association between personal distress and prosocial treatment of pain by exploring the possibility of a curvilinear relationship, as well as examining the potential moderating influence of both 69 individual (i.e., emotional processing traits) and contextual factors (i.e., personal cost of helping). Contrary to the first hypothesis and findings in the literature, personal distress did not emerge as a significant predictor of helping behaviour, and this relationship remained nonsignificant even when a curvilinear relationship was explored (Study 1). However, when levels of personal distress were simultaneously considered alongside individual differences in empathic concern, third-person pain perception, emotion regulation, and psychopathic traits, preliminary evidence for the moderating role of individual factors was found. Specifically, Studies 1 and 2 identified three replicable and distinct emotional processing profiles in undergraduate students and medical residents: Sensitive Responders, Indifferent Responders, and Insensitive Responders. Moreover, these profiles were found to be differently related to prosocial behaviour. The moderating role of personal cost of helping (i.e., method of remuneration) was also examined in Study 2. Although personal cost of helping was found to influence prosocial behaviour, it did not appear to moderate the relationship with empathy, contrary to expectation. Indeed, when comparing the effects of participant group (i.e., students or residents), incentive group (salary or capitation), and emotional processing profiles on prosocial behaviour, only emotional processing emerged as significant. Overall, these findings suggest that individual differences in emotional processing may play an important role in the prosocial treatment of pain. Moreover, these findings highlight the importance of considering these traits simultaneously, using person-centered approaches, as opposed to examining these variables in isolation. 4.1 Empathy and Prosocial Behaviour Despite a growing body of evidence identifying empathy as an important factor in both the perception and treatment of pain in others, the complexities of this relationship 70 remain unclear (e.g., Gleichgerrcht & Decety, 2014; Howell, 2016; Kaseweter, Drwecki, & Prkachin, 2012; Neumann et al., 2011; Prkachin et al., 2015; Rakel et al., 2007). As previously discussed, findings in the literature have been mixed, linking both high and low levels of personal distress with a greater propensity to help (e.g., Cialdini et al., 1987; Coll et al., 2017; Hein et al., 2011). In an effort to explain these paradoxical relationships, a curvilinear relationship between personal distress and prosocial behaviour was hypothesized. Because Pearson-r correlations can only detect linear relationships, curvilinear relationships can drastically reduce correlation coefficients, erroneously giving the appearance that the variables are unrelated (Goodwin & Leech, 2006; Wilson & Jones, 2016). Further, correlations can appear falsely high and inconsistent when curvilinear relationships co-occur with a restriction of range (e.g., Wilson & Jones, 2016). For example, if the relationship between personal distress and prosocial behaviour is U-shaped (i.e., curvilinear), and the sample contains individuals with generally low levels of personal distress, a negative correlation would be detected. Conversely, the correlation would appear positive in a sample high in personal distress. As such, a curvilinear relationship would offer a potential explanation for the opposing relationships found in the literature. Contrary to what was hypothesized, personal distress was not found to be associated with prosocial behaviour: no significant linear or curvilinear relationship was detected. This null finding is in contrast to the results of Coll et al. (2017), who observed a negative association between personal distress and prosocial behaviour. Importantly, the present studies employed the same pain stimuli and prosocial rating scale utilized by Coll et al., making this finding even more surprising. As noted previously, however, slight modifications were made to the prosocial task in an effort to increase ecological validity. Specifically, in 71 the Coll et al. study, the maximum waiting period for each trial was 20 seconds. In the present studies, the decision to help was associated with longer wait-times (i.e., maximum 60 seconds per trial), as well as a âreal-effortâ task in which participants were asked to respond to a question during the waiting period. In doing so, the decision to help each patient came at a greater personal cost to participants in the present studies (i.e., an increased workload and longer task duration). As previously mentioned, research has found that when the decision to help comes at a personal cost to the observer, helping behaviour is associated with empathic concern, as opposed to personal distress. Indeed, significant and positive correlations were observed between empathic concern and prosocial behaviour in both Study 1 and Study 2. While these modifications may have resulted in the inability to detect the influence of personal distress, they served to more closely approximate the personal cost associated with real healthcare decisions. As such, the observed correlations with empathic concern may more closely reflect the relationships that would be observed in healthcare settings. Another important difference to note is that in the Coll et al. study, personal distress levels in healthcare professionals were considerably lower (M = 14.73) than observed by medical residents in the present study (M = 18.01). One likely explanation for this difference is that the healthcare professionals in the Coll et al. study had an average of 11 years of clinical experience, whereas in the current study, participants were in their first or second year of residency. In fact, a recent study found that clinical exposure (i.e., years of experience and number of patients) was associated with lower levels of personal distress (Putrino, Tabullo, Mesurado, & de Minzi, 2018). As such, the range of personal distress levels in the current study may have been truncated, decreasing the ability to detect associations that may occur at lower levels of personal distress. As such, although the current findings did not 72 detect a linear or curvilinear relationship between personal distress and prosocial behaviour, it does not exclude the possibility that a relationship exists under different conditions. Additional studies are needed to further investigate these effects, ideally including the use of real patient-provider interactions and sample diverse in clinical exposure. Lastly, although empathic concern was found to be significantly associated with prosocial helping behaviour across both studies, stronger bivariate correlations were consistently found for third-person pain sensitivity, response bias, and coldheartedness. Specifically, higher levels of both pain sensitivity and response bias, as well as lower levels of coldheartedness, were related to an increased willingness to help. These findings suggest that traits other than empathy may have a more influential role in the processes involved in perceiving and decoding othersâ suffering, ultimately affecting how one responds to othersâ pain (e.g., help or ignore) â in line with the framework proposed by Prkachin et al. (2015). As such, it is important to examine how these traits interact together when exploring variability in the assessment and treatment of pain. 4.2 Emotional Response Profiles In Study 1, LPA was used to simultaneously examine response patterns across six indicator variables in a sample of undergraduate students: personal distress, empathic concern, pain sensitivity, cognitive reappraisal, self-centered impulsivity, and coldheartedness. In Study 2, a cross-validation of the LPA was performed using a separate sample of undergraduate studies and healthcare professionals. Both studies revealed three naturally occurring and distinct groups of emotional responding: Sensitive Responders, Indifferent Responders, and Insensitive Responders. The structure of the emotional response profiles was similar across both studies. 73 Importantly, these profiles were found to be predictive of helping behaviour during the prosocial behaviour task. Specifically, across both studies, Sensitive Responders exhibited the highest levels of helping behaviour, followed by Indifferent Responders and Insensitive Responders, respectively. In Study 2, however, the level of help did not significantly differ between Sensitive and Insensitive Responders. An encouraging finding is that Insensitive Responders were comprised of the smallest proportion of participants â indicating that the majority of participants fell into the groups engaging in higher levels of prosocial behaviour. As described previously, the nonsignificant difference in helping behaviour observed between Sensitive and Indifferent Responders may seem counterintuitive given their different profile structures. Specifically, Indifferent Responders were lower in empathic concern and cognitive reappraisal, and higher in self-centered impulsivity, coldheartedness, and personal distress. One potential explanation for this finding is that although group members both exhibited prosocial behaviour, it may have been elicited by different motives. For example, there are two theories of costly helping: arousal-reduction and empathy-altruism (Batson, 1987; Batson et al., 1993). The first theory suggests that prosocial behaviour is motivated by the desire to alleviate personal discomfort, whereas the latter suggests that altruistic behaviour is evoked by empathic concern. In other words, Indifferent Responders may engage in prosocial behaviour to reduce their heightened levels of personal distress, and Sensitive Responders may have motivated by their empathic concern for the patients in pain. These preliminary findings extend the current literature in important ways. To the best of my knowledge, this is the first study to employ person-centered approaches to simultaneously examine the influence of these emotional response traits on the prosocial 74 treatment of pain. Moreover, a common criticism of LPA concerns the extent to which latent profiles found in one study can be replicated in other samples. By cross-validating the solution in a separate sample of undergraduate students and medical residents, this study adds confirmatory evidence to the stability of the emotional response profiles. As previously noted, however, lower levels of personal distress may not have been adequately represented in these studies. Further, the sample of healthcare professionals were all completing their residency training in Family Medicine. The inclusion of samples from other medical disciplines, with a greater range of scores across the trait continuum, may elucidate profiles not revealed in the current studies. Additionally, future studies may wish to add other personality variables to further characterize emotional response profiles. 4.3 Personal Cost of Helping The decision to help when faced with othersâ pain is not only influenced by traits of the observer, but also by the context of the situation. One contextual factor that has previously been found to play an important role in prosocial behaviour is the personal cost associated with the decision to help (e.g., FeldmanHall et al., 2015; Godager & Wiesen, 2013; Hein et al., 2011). Importantly, when healthcare providers choose to offer help to patients in pain, they may be doing so at the personal cost of increasing their workload and decreasing their pay. For example, physicians with pure capitation contracts (e.g., family physicians in Alberta) are paid depending on the number of patients seen during a time-period. A physician opting to spend more time with a patient would be doing so at the personal cost of seeing fewer patients, resulting in reduced earnings. In fact, numerous studies have found that method of remuneration for physicians is an important contributor to the efficiency, quality, and quantity of health care delivery (e.g., Battista & Spitzer, 1983; 75 Forsberg et al., 2001; Glazier, Klein-Geltink, Kopp, & Sibley, 2009; Godager & Wiesen, 2013; Rudmik et al., 2014; Wranik, 2012). Moreover, research suggests that the personal cost of helping may influence the relationship between empathy and prosocial behaviour. As noted, FeldmanHall et al. (2015) found that when the decision to help came at a personal financial cost, empathic concern â not personal distress â was predictive of helping behaviour. As such, a primary aim of Study 2 was to examine the moderating influence of personal cost of help, specifically focusing on method of remuneration. In line with previous studies, method of remuneration was found to influence prosocial behaviour (e.g., Godager & Wiesen, 2013; Rudmik et al., 2014; Wranik, 2012). Specifically, individuals in the salary condition saw fewer patients than individuals in the capitation condition and offered significantly more help to patients in pain. Method of remuneration, however, did not appear to moderate the relationship between empathy and helping behaviour. Although the relationship between empathic concern and help ratings was slightly stronger in the capitation condition than the salary condition (in line with the hypothesis), both coefficients were significant, and the difference was minimal. One potential explanation for this finding is that due to the nature of the task, the decision to help came at a personal cost regardless of the incentive condition. Specifically, as noted previously, the prosocial task was modified to include longer wait-times and a real-effort task to increase ecological validity. In doing so, the personal cost of helping was greater than that in the Coll et al. (2017) study, potentially reducing the ability to detect any moderating effects of incentive. Indeed, when comparing help ratings between Study 2 (i.e., MHelp = 49%) and the Coll et al. study (i.e., MHelp = ~68%), it is apparent that participants in the present study were 76 less willing to offer help to patients in pain â likely a result of the increased workload and prolonged waiting period. 4.4 Implications The results of this study contribute to a growing body of literature that may have important implications within the field of healthcare. As discussed previously, pain has been found to be drastically undertreated by healthcare professionals (Curtis & Morrell, 2006; Decosterd et al., 2007; Pines & Hollander, 2008; Stalnikowicz et al., 2005; Todd et al., 2007), leading to long-term emotional and physical consequences for many patients (Herr & Garand, 2001; Jones & Macfarlane, 2005). Moreover, repeated exposure to patients in pain can have detrimental effects for physiciansâ performance and well-being (Gleichgerrcht & Decety, 2013; West et al., 2006). Given the high prevalence of pain-related visits, and the risk they pose to both patients and healthcare professionals, there is a clear and pressing need to understand the factors that contribute to effective pain care. For example, though research suggests low levels of personal distress could serve as a protective factor against burnout in physiciansâ (Gleichgerrcht & Decety, 2013), while resulting in prosocial treatment of pain (Coll et al., 2017), this may not hold true for all individuals or across all situations. Indeed, in the current study, the emotional response profile lowest in personal distress exhibited the lowest levels of helping behaviours. This association may have occurred due to the specific combination of traits characterizing Insensitive Responders â namely, low levels of personal distress, empathic concern, and pain sensitivity, as well as high levels of self-centered impulsivity and coldheartedness. In other words, advising physicians to emotionally detach from their patientsâ pain may be effective for some individuals while harmful to others. 77 Promisingly, researchers have found evidence to suggest that the combination of high levels of both perspective taking and empathic concern can be protective against burnout in physicians (Lamothe, Boujut, Zenasni, & Sultan, 2014). In the current studies, Sensitive Responders were found to exhibit the highest levels of perspective taking and empathic concern, as well as the greatest level of helping behaviour. Together, these findings suggest that incorporating interventions in medical curriculum to promote perspective taking and empathic concern may aid in the prevention of burnout and facilitate prosocial pain treatment, reducing the risk to both patients and providers. One intervention that may be beneficial is mindfulness training. Recently, researchers conducted a series of four studies demonstrating that mindfulness training increased prosociality towards strangers, as mediated by increased levels of empathic concern (Berry et al., 2018). Notably, these effects were observed using a very brief training method involving an 8-minute, audio-recorded series of instructions. Additionally, across the present studies, Sensitive Responders were characterized by the highest levels of pain sensitivity. Recent findings suggest that a manual-based training intervention (i.e., Index of Facial Pain Expression) significantly improved observersâ ability to detect pain expressions after as few as 3.5 hours (Rash, Prkachin, Solomon, & Campbell, 2019). Such short, yet effective, interventions may hold promise for improving pain care, without overburdening medical students and faculty. Moreover, although method of remuneration was not found to moderate the relationship between empathy and prosocial behaviour in Study 2, participants in the salary condition offered significantly more help to patients in pain than those in the capitation condition. This finding is in line with previous studies demonstrating that although salary offers no incentive for quantity of care, it appears to promote quality of care (e.g., Gosdene et 78 al., 2003; Lagarde & Blaauw, 2017). Consequently, salary and fee-for-service based payments may be preferable over capitation to facilitate the prosocial treatment of pain and increase patient satisfaction (e.g., Lagarde & Blaauw, 2017; Sorbero et al., 2003). Together, the results of this dissertation suggest that in order to develop effective pain treatment strategies, it is important to consider combinations of personality traits found in healthcare providers, as well the context in which the help is occurring. In other words, a one-size-fits-all approach to pain treatment may not be appropriate. 4.5 Strengths, Limitations, and Future Directions This study had several strengths and limitations that should be considered when interpreting the results. First, an important strength of this study is that heterogeneity in prosocial behaviour was explored by simultaneously examining the influence of several emotional response traits, as well as the context under which help occurs (i.e., incentive condition). The majority of studies to date have examined these traits in isolation, using variable-centered approaches. As noted, even when several variables are explored as potential moderators through variable-centered approached, Type I error rates are inflated, and interactions become increasingly difficult to detect and interpret (Bauer & Shanahan, 2007; Lanza & Rhoads, 2013). In the present studies, a person-centered approach (i.e., LPA) was employed, allowing for the interactions among multiple variables to be examined concurrently. In doing so, individual response patterns could be identified that would be unobservable when analyzed by linear models. A second important strength of this study is the way in which prosocial behaviour was assessed. As mentioned, an important criticism of many health studies is that the designs often lack ecological validity (Lagarde & Blaauw, 2017). For example, studies examining 79 prosocial behaviour often utilize self-report measures of prosocial tendency or use paradigms that fail to capture the benefits and costs associated with the decision to help (e.g., Lagarde & Blaauw, 2017; Lockwood et al., 2014). To help circumvent this issue, a modified version of the prosocial behaviour task designed by Coll et al. (2017) was used. Importantly, the decision to help in this behavioural task is associated with both benefits and consequences. Specifically, help was not only associated with increased workload and longer wait times, but also with visual feedback displaying the pain-relief provided to the patient. Additionally, stimuli in both the prosocial behaviour and third-person pain task contained facial expressions of real patients in pain from the UNBC-McMaster Shoulder Pain Archive. The use of genuine and spontaneous facial expressions is important, as they have been found to meaningfully differ from posed expressions (e.g. Bartlett et al., 2006; Hill & Craig, 2002; Zhang et al., 2014). Although considerable effort was made to improve the realism and ecological validity, the paradigm was still subjected to the limitations of a controlled lab setting and any generalizations to the real-world should be made with caution, if at all. While the use of stimuli from the UNBC-McMaster Should Pain database is a strength of the study, it should be noted that that the expressions elicited were from acute pain. Although similar pain specific muscle movements have been found to occur for acute, chronic, and oncological pain, it is possible that observersâ behavioural responses may differ as a function of pain type (e.g., Chen, Ansari, & Wilkie, 2018; Wilkie, 1995). Consequently, future studies should include other types of pain stimuli. Another limitation of this study that should be noted is the use of self-report measures to assess emotional processing traits. Despite their widespread use in the assessment of personality traits, self-report measures can be prone to bias, such as social desirability and 80 inattentive responding (e.g., Caputo, 2017; Holtgraves, 2017; Watts et al., 2016). Additionally, self-report measures were collected after the experimental tasks to avoid potentially priming behaviour. Consequently, the decision to help or not may have influenced responses on these measures, such as the amount of empathy reported. Future studies should assess these traits prior to measuring prosocial behaviour. Moreover, this study examined only dispositional empathy (i.e., trait). Future studies should include situational or state empathy measures to gain a deeper understanding of the relationship between empathy and prosocial behaviour. Additionally, the sample for this study involved a relatively small number of Family Medicine residents, and prosocial behaviour was operationalized as amount of time spend with patients. As such, findings from this study may not extend to other healthcare specialties or modes of pain treatment. To gain a deeper understanding, an interesting line of future research would be to examine emotional response profiles in other types of physicians (e.g., Emergency Medicine), and the influence of these profiles on other aspects of treatment such as type of medication prescribed. Lastly, it should be noted that although implicit biases were not directly examined, they are likely an important cause of treatment disparities in healthcare and may have influenced the decision to help in the current studies. Indeed, findings from a systematic review revealed that implicit biases are prevalent in healthcare providers (i.e., physicians and nurses), at a degree similar to the general population, and that these biases likely influence diagnosis and treatment decisions (FitzGerald & Hurst, 2017). For example, Green et al., (2007) examined a sample of medical residents and found that as pro-White implicit bias increased, so did the likelihood of recommending medical intervention (i.e., thrombolysis) 81 for White patients and not Black patients. In the current study, pain intensity and help ratings may have been influenced by implicit biases pertaining to patient characteristics, such as race, gender, age, and weight. As such, future studies may wish to include measures of implicit biases to examine how these biases influences the results and potentially interact with the latent profiles. 4.6 Conclusion Healthcare providers are often faced with the difficult job of identifying and treating pain. Indeed, patientsâ pain has been found to be commonly undertreated by healthcare professionals. This inadequate treatment of pain can bear long-term negative consequences for the patient, including increased rates of depression, anxiety, sleep disturbances, and reduced quality of life (Herr & Garand, 2001; Jones & Macfarlane, 2005; Sinatra, 2010). Overall, findings from this dissertation suggest that both factors pertaining to the individual and context may play an important role in the prosocial treatment of pain. Specifically, three emotional response profiles were identified based on individual differences in empathy, third-person pain perception, emotion regulation, and psychopathic traits. These profiles were found to differentially predict willingness to engage in helping behaviour. Moreover, prosocial behaviour was found to differ depending on the method of remuneration (i.e., salary or capitation). Identifying factors associated with effective pain treatment may help address the underestimation and undertreatment bias associated with pain, conceivably reducing the suffering of patients and improving outcomes. 82 References Ashar, Y. K., Andrews-Hanna, J. R., Yarkoni, T., Sills, J., Halifax, J., Dimidjian, S., & Wager, T. D. (2016). Effects of compassion meditation on a psychological model of charitable donation. Emotion, 16(5), 691-705. Avenanti, A., Minio-Paluello, I., Sforza, A., & Aglioti, S. M. (2009). Freezing or escaping? Opposite modulations of empathic reactivity to the pain of others. Cortex, 45(9), 1072-1077. Bartlett, M. S., Littlewort, G., Frank, M. G., Lainscsek, C., Fasel, I. R., & Movellan, J. R. (2006). Automatic recognition of facial actions in spontaneous expressions. Journal of Multimedia, 1(6), 22â35. Bartley, E. J., Boissoneault, J., Vargovich, A. M., Wandner, L. D., Hirsh, A. T., Lok, B. C., â¦ Robinson, M. E. (2015). The influence of health care professional characteristics on pain management decisions. Pain Medicine, 16, 99-111 Batson, C. D. (1987). Prosocial motivation: Is it ever truly altruistic?. Advances in Experimental Social Psychology, 20, 65-122. Batson, C. D., Batson, J. G., Slingsby, J. K., Harrell, K. L., Peekna, H. M., & Todd, R. M. (1991). Empathic joy and the empathy-altruism hypothesis. Journal of Personality and Social Psychology, 61(3), 413. Battista, R. N., & Spitzer, W. O. (1983). Adult cancer prevention in primary care: contrasts among primary care practice settings in Québec. American Journal of Public Health, 73(9), 1040-1041. 83 Bauer, D. J., & Shanahan, M. J. (2007). Modeling complex interactions: Person-centered and variable-centered approaches. In T. Little, J. Bovaird & N. Card (Eds.), Modeling ecological and contextual effects in longitudinal studies of human development (pp. 255-283). Mahwah, NJ: Lawrence Erlbaum. Beckman, T. J., Reed, D. A., Shanafelt, T. D., & West, C. P. (2012). Resident physician well-being and assessments of their knowledge and clinical performance. Journal of General Internal Medicine, 27(3), 325-330. Benning, S. D., Patrick, C. J., Hicks, B. M., Blonigen, D. M., & Krueger, R. F. (2003). Factor structure of the psychopathic personality inventory: validity and implications for clinical assessment. Psychological Assessment, 15(3), 340-350. Bernardes, S. F., Costa, M., & Carvalho, H. (2013). Engendering pain management practices: the role of physician sex on chronic low-back pain assessment and treatment prescriptions. The Journal of Pain: Official Journal of the American Pain Society, 14, 931-940. Berry, D. R., Cairo, A. H., Goodman, R. J., Quaglia, J. T., Green, J. D., & Brown, K. W. (2018). Mindfulness increases prosocial responses toward ostracized strangers through empathic concern. Journal of Experimental Psychology: General, 147(1), 93-112. Caes, L., Goubert, L., Devos, P., Verlooy, J., Benoit, Y., & Vervoort, T. (2016). Personal distress and sympathy differentially influence health care professional and parentsâ estimation of child procedure-related pain. Pain Medicine, 18(2), 275-282. 84 Caes, L., Uzieblo, K., Crombez, G., De Ruddere, L., Vervoort, T., & Goubert, L. (2012). Negative emotional responses elicited by the anticipation of pain in others: psychophysiological evidence. The Journal of Pain, 13(5), 467-476. Calder, A. J., Keane, J., Manly, T., Sprengelmeyer, R., Scott, S., Nimmo-Smith, I., & Young, A. W. (2003). Facial expression recognition across the adult life span. Neuropsychologia, 41(2), 195-202. Caputo, A. (2017). Social desirability bias in self-reported well-being measures: Evidence from an online survey. Universitas Psychologica, 16(2), 245-255. Carlo, G., & Randall, B. A. (2002). The development of a measure of prosocial behaviors for late adolescents. Journal of Youth and Adolescence, 31(1), 31-44. Chen, Z., Ansari, R., & Wilkie, D. (2018). Automated Pain Detection from Facial Expressions using FACS: A Review. In SPIE Medical Imaging. International Society for Optics and Photonics. Chen, E. H., Shofer, F. S., Dean, A. J., Hollander, J. E., Baxt, W. G., Robey, J. L., â¦ Mills, A. M. (2008). Gender disparity in analgesic treatment of emergency department patients with acute abdominal pain. Academic Emergency Medicine, 15, 414-418. Cialdini, R. B., Schaller, M., Houlihan, D., Arps, K., Fultz, J., & Beaman, A. L. (1987). Empathy-based helping: Is it selflessly or selfishly motivated?. Journal of Personality and Social Psychology, 52(4), 749. Cleeland, C. S., Gonin, R., Baez, L., Loehrer, P., & Pandya, K. J. (1997). Pain and treatment of pain in minority patients with cancer: The Eastern Cooperative Oncology Group Minority Outpatient Pain Study. Annals of Internal Medicine, 127(9), 813-816. 85 Coll, M.P., Budell, L., Rainville, P., Decety, J., & Jackson, P. L. (2012). The role of gender in the interaction between self-pain and the perception of pain in others. The Journal of Pain, 13, 695-703. Coll, M. P., Grégoire, M., Eugène, F., & Jackson, P. L. (2017). Neural correlates of prosocial behavior towards persons in pain in healthcare providers. Biological Psychology. Coll, M. P., Grégoire, M., Prkachin, K. M., & Jackson, P. L. (2016). Repeated exposure to vicarious pain alters electrocortical processing of pain expressions. Experimental Brain Research, 234(9), 2677-2686. Cordell, W. H., Keene, K. K., Giles, B. K., Jones, J. B., Jones, J. H., & Brizendine, E. J. (2002). The high prevalence of pain in emergency medical care. The American Journal of Emergency Medicine, 20(3), 165-169. Curtis, L., & Morrell, T. (2006). Pain management in the emergency department. Emergency Medicine Practice, 8(7), 1â28. Danziger, N., Prkachin, K. M., & Willer, J. C. (2006). Is pain the price of empathy? The perception of others' pain in patients with congenital insensitivity to pain. Brain, 129(9), 2494-2507. Daubresse, M., Chang, H. Y., Yu, Y., Viswanathan, S., Shah, N. D., Stafford, R. S., ... & Alexander, G. C. (2013). Ambulatory diagnosis and treatment of non-malignant pain in the United States, 2000â2010. Medical Care, 51(10). Davis, M. (1980). A multidimensional approach to individual differences in empathy. Catalog of Selected Documents in Psychology, 10(4), 1-17. Decety, J. (2011). Dissecting the neural mechanisms mediating empathy. Emotion Review, 3(1), 92-108. 86 Decety, J., & Lamm, C. (2006). Human empathy through the lens of social neuroscience. The Scientific World Journal, 6, 1146-1163. Decety, J., Chen, C., Harenski, C., & Kiehl, K. A. (2013). An fMRI study of affective perspective taking in individuals with psychopathy: imagining another in pain does not evoke empathy. Frontiers in Human Neuroscience, 7, 489-489. Decosterd, I., Hugli, O., Tamchès, E., Blanc, C., Mouhsine, E., Givel, J. C., ... & Buclin, T. (2007). Oligoanalgesia in the emergency department: short-term beneficial effects of an education program on acute pain. Annals of Emergency Medicine, 50(4), 462-471. Derksen, F., Bensing, J., & Lagro-Janssen, A. (2013). Effectiveness of empathy in general practice: a systematic review. The British Journal of General Practice, 63(606), e76-e84. Devlin, R. A., & Sarma, S. (2008). Do physician remuneration schemes matter? The case of Canadian family physicians. Journal of Health Economics, 27(5), 1168-1181. Deyo, K. S., Prkachin, K. M., & Mercer, S. R. (2004). Development of sensitivity to facial expression of pain. Pain, 107(1), 16-21. Dimatteo, M. R., & Martin, L. R. (2002). Health Psychology. Boston: Allyn & Bacon. Donahue, J. J., McClure, K. S., & Moon, S. M. (2014). The relationship between emotion regulation difficulties and psychopathic personality characteristics. Personality Disorders, 5(2), 186-194. Dovidio, JF. and Penner, LA. (2004). Helping and Altruism. In: Brewer MB, Hewstone M, editors. Emotion and Motivation. Malden: Blackwell Publishing, 247-80. Ducharme, J., Tanabe, P., Homel, P., Miner, J. R., Chang, A. K., Lee, J., ... & PEMI Study Group. (2008). The influence of triage systems and triage scores on timeliness of ED 87 analgesic administration. The American Journal of Emergency Medicine, 26(8), 867-873. Eisenbarth, H., Lilienfeld, S. O., & Yarkoni, T. (2015). Using a genetic algorithm to abbreviate the Psychopathic Personality InventoryâRevised (PPI-R). Psychological Assessment, 27(1), 194. Eisenberg, N., & Fabes, R. A. (1992). Emotion, regulation, and the development of social competence. Review of Personality and Social Psychology, 14, 119-150. Ekman, P., & Friesen, W. V. (1978). Manual for the Facial Action Coding System. Consulting Psychologists Press. Eng, J. (2005). Receiver operating characteristic analysis. Academic radiology, 12(7), 909-916. Evans, B. J., Stanley, R. O., & Burrows, G. D. (1993). Measuring medical students' empathy skills. Psychology and Psychotherapy: Theory, Research and Practice, 66(2), 121-133. Falkenbach, D. M., Balash, J., Tsoukalas, M., Stern, S., & Lilienfeld, S. O. (2018). From theoretical to empirical: Considering reflections of psychopathy across the thin blue line. Personality Disorders, 9(5), 420-428. FeldmanHall, O., Dalgleish, T., Evans, D., & Mobbs, D. (2015). Empathic concern drives costly altruism. Neuroimage, 105, 347-356. FitzGerald, C., & Hurst, S. (2017). Implicit bias in healthcare professionals: a systematic review. BMC Medical Ethics, 18(1), 19. Forsberg, E., Axelsson, R., & Arnetz, B. (2001). Financial incentives in health care. The impact of performance-based reimbursement. Health Policy, 58(3), 243-262. 88 Glazier, R. H., Klein-Geltink, J., Kopp, A., & Sibley, L. M. (2009). Capitation and enhanced fee-for-service models for primary care reform: a population-based evaluation. Canadian Medical Association Journal, 180(11), E72-E81. Gleichgerrcht, E., & Decety, J. (2013). Empathy in clinical practice: how individual dispositions, gender, and experience moderate empathic concern, burnout, and emotional distress in physicians. PloS One, 8(4), e61526. Gleichgerrcht, E., & Decety, J. (2014). The relationship between different facets of empathy, pain perception and compassion fatigue among physicians. Frontiers in Behavioral Neuroscience, 8. Godager, G., & Wiesen, D. (2013). Profit or patientsâ health benefit? Exploring the heterogeneity in physician altruism. Journal of Health Economics, 32(6), 1105-1116. Goodwin, L. D., & Leech, N. L. (2006). Understanding correlation: Factors that affect the size of r. The Journal of Experimental Education, 74(3), 249-266. Gosden, T., Sibbald, B., Williams, J., Petchey, R., & Leese, B. (2003). Paying doctors by salary: a controlled study of general practitioner behaviour in England. Health Policy, 64(3), 415-423. Guru, V., & Dubinsky, I. (2000). The patient vs. caregiver perception of acute pain in the emergency department. The Journal of Emergency Medicine, 18(1), 7-12. Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond, K. L., Iezzoni, L. I., & Banaji, M. R. (2007). Implicit bias among physicians and its prediction of thrombolysis decisions for black and white patients. Journal of General Internal Medicine, 22(9), 1231-1238. 89 Hadjistavropoulos, T., Craig, K. D., Duck, S., Cano, A., Goubert, L., Jackson, P. L., ... & Vervoort, T. (2011). A biopsychosocial formulation of pain communication. Psychological Bulletin, 137(6), 910-939. Hall, J. A. (2011). Cliniciansâ accuracy in perceiving patients: its relevance for clinical practice and a narrative review of methods and correlates. Patient Education and Counseling, 84(3), 319-324. Hein, G., Lamm, C., Brodbeck, C., & Singer, T. (2011). Skin Conductance Response to the Pain of Others Predicts Later Costly Helping. PLoS One, 6(8), e22759. Herr, K. A., & Garand, L. (2001). Assessment and measurement of pain in older adults. Clinics in Geriatric Medicine, 17(3), 457. Hill, M. L., & Craig, K. D. (2002). Detecting deception in pain expressions: the structure of genuine and deceptive facial displays. Pain, 98(1), 135-144. Hoffman, M. L. (1991). Is empathy altruistic?. Psychological Inquiry, 2(2), 131-133. Hoffmann, D. E., & Tarzian, A. J. (2001). The girl who cried pain: A bias against women in the treatment of pain. Journal of Law, Medicine & Ethics, 29, 13-27. Holtgraves, T. (2017). Social desirability and the interpretation of uncertainty terms in selfâreport questions. Applied Cognitive Psychology, 31(6), 623-631. Howell, S. J. (2016). Empathy and its role in quality of care. Osteopathic Family Physician, 8(4), 20-23. Iversen, T., & Lurås, H. (2000). The effect of capitation on GPs' referral decisions. Health Economics, 9(3), 199-210. 90 Jensen, M. P., & Karoly, P. (2011). Self-report scales and procedures for assessing pain in adults. In D. C. Turk & R. Melzack (Eds.) Handbook of Pain Assessment. (3rd ed., pp. 19-44). New York: Guilford Press. Johnson, M. M., Caron, K. M., Mikolajewski, A. J., Shirtcliff, E. A., Eckel, L. A., & Taylor, J. (2014). Psychopathic traits, empathy, and aggression are differentially related to cortisol awakening response. Journal of Psychopathology and Behavioral Assessment, 36(3), 380-388. Jones, G., & Macfarlane, G. (2005). Epidemiology of pain in older persons. In S. Gibson & D. Weiner (Eds.), Pain in older persons (pp. 3-22). Seattle: IASP Press. Kaseweter, K. (2015). Insensitivity to suffering: The relation of psychopathic traits to somatic processing, first-person and third-person pain (Masterâs thesis). Retrieved from http://unbc.arcabc.ca. Kaseweter, K. A., Drwecki, B. B., & Prkachin, K. M. (2012). Racial differences in pain treatment and empathy in a Canadian sample. Pain Research and Management, 17(6), 381-384. Kaseweter, K., Rose, K., Bednarik, S., & Woodworth, M. (2020). More than meets the eye: the role of psychopathic traits in attention to distress. Psychology, Crime & Law, 26(2), 109-127. Kelley, S. E., Edens, J. F., Donnellan, M. B., Ruchensky, J. R., Witt, E. A., & McDermott, B. E. (2016). Development and validation of an inconsistent responding scale for an abbreviated version of the Psychopathic Personality InventoryâRevised. Personality and Individual Differences, 91, 58-62. 91 Lagarde, M., & Blaauw, D. (2017). Physiciansâ responses to financial and social incentives: A medically framed real effort experiment. Social Science & Medicine, 179, 147-159. Lamm, C., Batson, C. D., & Decety, J. (2007). The neural substrate of human empathy: effects of perspective-taking and cognitive appraisal. Journal of Cognitive Neuroscience, 19(1), 42-58. Lamothe, M., Boujut, E., Zenasni, F., & Sultan, S. (2014). To be or not to be empathic: the combined role of empathic concern and perspective taking in understanding burnout in general practice. BMC Family Practice, 15(1), 1-7. Lanza, S. T., & Rhoades, B. L. (2013). Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14(2), 157-168. Lewén, H., Gardulf, A., & Nilsson, J. (2010). Documented assessments and treatments of patients seeking emergency care because of pain. Scandinavian Journal of Caring Sciences, 24(4), 764-771. Lockwood, P. L., Bird, G., Bridge, M., & Viding, E. (2013). Dissecting empathy: high levels of psychopathic and autistic traits are characterized by difficulties in different social information processing domains. Frontiers in Human Neuroscience, 7, 760. Lockwood, P. L., Seara-Cardoso, A., & Viding, E. (2014). Emotion regulation moderates the association between empathy and prosocial behavior. PloS One, 9(5), e96555. Lubke, G., & Muthén, B. O. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling, 14(1), 26-47. Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P. E., & Matthews, I. (2011, March). Painful data: The UNBC-McMaster shoulder pain expression archive database. 92 In Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on (pp. 57-64). IEEE. Marcoux, L. A., Michon, P. E., Voisin, J. I., Lemelin, S., Vachon-Presseau, E., & Jackson, P. L. (2013). The modulation of somatosensory resonance by psychopathic traits and empathy. Frontiers in Human Neuroscience, 7. Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. (2009). Classical latent profile analysis of academic self-concept dimensions: synergy of person-and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling, 16, 191â225. McFarland, D. C., Malone, A. K., & Roth, A. (2017). Acute empathy decline among resident physician trainees on a hematologyâoncology ward: an exploratory analysis of house staff empathy, distress, and patient death exposure. PsychoâOncology, 26(5), 698-703. McNicol D. (1972). A Primer of Signal Detection Theory, Vol. 8. London: Allen and Unwin. Mercer, S. W., & Reynolds, W. (2002). Empathy and quality of care. British Journal of General Practice, 52(Supple), 9-12. Michael, G. E., Sporer, K. A., & Youngblood, G. M. (2007). Women are less likely than men to receive prehospital analgesia for isolated extremity injuries. The American Journal of Emergency Medicine, 25, 901-906. Neumann, M., Edelhäuser, F., Tauschel, D., Fischer, M. R., Wirtz, M., Woopen, C., ... & Scheffer, C. (2011). Empathy decline and its reasons: a systematic review of studies with medical students and residents. Academic Medicine, 86(8), 996-1009. 93 Newton, B. W. (2013). Walking a fine line: is it possible to remain an empathic physician and have a hardened heart? Frontiers in Human Neuroscience, 7, 233. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural equation modeling: A multidisciplinary Journal, 14(4), 535-569. Pavey, L., Greitemeyer, T., & Sparks, P. (2012). âI help because I want to, not because you tell me toâ empathy increases autonomously motivated helping. Personality and Social Psychology Bulletin, 38(5), 681-689. Pines, J. M., & Hollander, J. E. (2008). Emergency department crowding is associated with poor care for patients with severe pain. Annals of Emergency Medicine, 51(1), 1-5. Price, D. D., McGrath, P. A., Rafii, A., & Buckingham, B. (1983). The validation of visual analogue scales as ratio scale measures for chronic and experimental pain. Pain, 17(1), 45-56. Prkachin, K. M. (1992). The consistency of facial expressions of pain: A comparison across modalities. Pain, 51(3), 297-306. Prkachin, K. M., & Mercer, S. R. (1989). Pain expression in patients with shoulder pathology: validity, properties and relationship to sickness impact. Pain, 39(3), 257-265. Prkachin, K. M., & Solomon, P. E. (2008). The structure, reliability and validity of pain expression: Evidence from patients with shoulder pain. Pain, 139(2), 267-274. Prkachin, K. M., Berzins, S., & Mercer, S. R. (1994). Encoding and decoding of pain expressions: A judgement study. Pain, 58(2), 253-259. 94 Prkachin, K. M., Kaseweter, K. A., & Browne, M. E. (2015). Understanding the suffering of others: the sources and consequences of third-person pain. In Pain, Emotion and Cognition (pp. 53-72). Springer International Publishing. Prkachin, K. M., Solomon, P. E., & Ross, J. (2007). Underestimation of pain by health-care providers: towards a model of the process of inferring pain in others. Canadian Journal of Nursing Research, 39(2), 88-106. Puntillo, K., Neighbor, M., OâNeil, N., & Nixon, R. (2003). Accuracy of emergency nurses in assessment of patientsâ pain. Pain Management Nursing, 4(4), 171-175. Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111-164. Rakel, D., Barrett, B., Zhang, Z., Hoeft, T., Chewning, B., Marchand, L., & Scheder, J. (2011). Perception of empathy in the therapeutic encounter: effects on the common cold. Patient Education and Counseling, 85(3), 390-397. Rash, J. A., Prkachin, K. M., & Campbell, T. S. (2015). Observer trait anxiety is associated with response bias to patient facial pain expression independent of pain catastrophizing. Pain Research and Management, 20(1), 39-45. Rash, J. A., Prkachin, K. M., Solomon, P. E., & Campbell, T. S. (2019). Assessing the efficacy of a manualâbased intervention for improving the detection of facial pain expression. European Journal of Pain, 23(5), 1006-1019. Reji, P. (2019). Do Psychopathic Traits Influence Distractibility by Empathy-Eliciting Pictures? (Masterâs thesis). CUNY Academic Works. Retrieved from https://academicworks.cuny.edu/jj_etds/123/ 95 Robinson, M. E., & Wise, E. A. (2003). Gender bias in the observation of experimental pain. Pain, 104, 259-264. doi:10.1016/S0304-3959(03)00014-9 Rosenberg, J., Beymer, P., Anderson, D., van Lissa, C. J., & Schmidt, J. (2019). tidyLPA: An R package to easily carry out Latent Profile Analysis (LPA) using open-source or commercial software. Journal of Open Source Software, 4(44), 978. Ruben, M. A., van Osch, M., & Blanch-Hartigan, D. (2015). Healthcare providersâ accuracy in assessing patientsâ pain: A systematic review. Patient Education and Counseling, 98(10), 1197-1206. Ruchensky, J. R., Edens, J. F., Donnellan, M. B., & Witt, E. A. (2017). Examining the reliability and validity of an abbreviated Psychopathic Personality InventoryâRevised (PPI-R) in four samples. Psychological Assessment, 29(2), 238. Rudmik, L., Wranik, D., & Rudisill-Michaelsen, C. (2014). Physician payment methods: a focus on quality and cost control. Journal of Otolaryngology-Head & Neck Surgery, 43(1), 34. Safdar, B., Heins, A., Homel, P., Miner, J., Neighbor, M., DeSandre, P., & Todd, K. H. (2009). Impact of physician and patient gender on pain management in the emergency departmentâA multicenter study. Pain Medicine, 10, 364-372. Schäfer, G., Prkachin, K. M., Kaseweter, K. A., & de C Williams, A. C. (2016). Health care providers' judgments in chronic pain: the influence of gender and trustworthiness. Pain, 157(8), 1618-1625. Shamay-Tsoory, S. G., Harari, H., Aharon-Peretz, J., & Levkovitz, Y. (2010). The role of the orbitofrontal cortex in affective theory of mind deficits in criminal offenders with psychopathic tendencies. Cortex, 46(5), 668-677. 96 Sinatra, R. (2010). Causes and consequences of inadequate management of acute pain. Pain medicine, 11(12), 1859-1871. Solomon, P. (2001). Congruence between health professionalsâ and patientsâ pain ratings: a review of the literature. Scandinavian Journal of Caring Sciences, 15(2), 174-180. Sorbero, M. E., Dick, A. W., Zwanziger, J., Mukamel, D., & Weyl, N. (2003). The Effect of Capitation on Switching Primary Care Physicians. Health Services Research, 38(1), 191-209. Sörman, K., Edens, J. F., Smith, S. T., Clark, J. W., Kristiansson, M., & Svensson, O. (2016). Boldness and its relation to psychopathic personality: Prototypicality analyses among forensic mental health, criminal justice, and layperson raters. Law and Human Behavior, 40(3), 337. Stalnikowicz, R., Mahamid, R., Kaspi, S., & Brezis, M. (2005). Undertreatment of acute pain in the emergency department: a challenge. International Journal for Quality in Health Care, 17(2), 173-176. Taku, K., & McLarnon, M. J. (2018). Posttraumatic growth profiles and their relationships with HEXACO personality traits. Personality and Individual Differences, 134, 33-42. Tein, J. Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural equation modeling: a multidisciplinary journal, 20(4), 640-657. Thompson, R. A. (1994). Emotion regulation: A theme in search of definition. Monographs of the Society for Research in Child Development, 59(2â3), 25-52. 97 Tice, D. M., Bratslavsky, E., & Baumeister, R. F. (2001). Emotional distress regulation takes precedence over impulse control: If you feel bad, do it!. Journal of Personality and Social Psychology, 80(1), 53. Todd, K. H., Ducharme, J., Choiniere, M., Crandall, C. S., Fosnocht, D. E., Homel, P., ... & PEMI Study Group. (2007). Pain in the emergency department: results of the pain and emergency medicine initiative (PEMI) multicenter study. The Journal of Pain, 8(6), 460-466. Vachon-presseau, E., Martel, M. O., Roy, M., Caron, E., Jackson, P. L., & Rainville, P. (2011). The multilevel organization of vicarious pain responses: effects of pain cues and empathy traits on spinal nociception and acute pain. Pain, 152(7), 1525-1531. Varrassi, G., Müller-Schwefe, G., Pergolizzi, J., Oronska, A., Morlion, B., Mavrocordatos, P., ... & Collett, B. (2010). Pharmacological treatment of chronic painâthe need for change. Current Medical Research and Opinion, 26(5), 1231-1245. Wandner, L. D., Heft, M. W., Lok, B. C., Hirsh, A. T., George, S. Z., Horgas, A. L., â¦ Robinson, M. E. (2014). The impact of patientsâ gender, race, and age on health care professionalsâ pain management decisions: An online survey using virtual human technology. International Journal of Nursing Studies, 51, 726-733. Watts, A. L., Lilienfeld, S. O., Edens, J. F., Douglas, K. S., Skeem, J. L., Verschuere, B., & LoPilato, A. C. (2016). Does response distortion statistically affect the relations between self-report psychopathy measures and external criteria?. Psychological Assessment, 28(3), 294. 98 West, C. P., Huschka, M. M., Novotny, P. J., Sloan, J. A., Kolars, J. C., Habermann, T. M., & Shanafelt, T. D. (2006). Association of perceived medical errors with resident distress and empathy: a prospective longitudinal study. Jama, 296(9), 1071-1078. White, B. A. (2014). Who cares when nobody is watching? Psychopathic traits and empathy in prosocial behaviors. Personality and Individual Differences, 56, 116-121. Wilkie, D. J. (1995). Facial expressions of pain in lung cancer. Analgesia, 1(2), 91-99. Wilson, J. H., & Joye, S. W. (2016). Research methods and statistics: An Integrated Approach. Thousand Oaks: SAGE Publications. Wranik, D. (2012). Healthcare policy tools as determinants of health-system efficiency: evidence from the OECD. Health Economics, Policy and Law, 7(2), 197-226. Wranik, D. W., & Durier-Copp, M. (2010). Physician remuneration methods for family physicians in Canada: expected outcomes and lessons learned. Health Care Analysis, 18(1), 35-59. Yarnold, P. R., Bryant, F. B., Nightingale, S. D., & Martin, G. J. (1996). Assessing physician empathy using the Interpersonal Reactivity Index: A measurement model and cross-sectional analysis. Psychology, Health & Medicine, 1(2), 207-221. Zahedzadeh, G., Barraza, J. A., & Zak, P. J. (2017). Persuasive narratives and costly actions. Terrorism and Political Violence, 29(1), 160-172. Zhang, X., Yin, L., Cohn, J. F., Canavan, S., Reale, M., Horowitz, A., ... Girard, J. M. (2014). Bp4d-spontaneous: A high-resolution spontaneous 3d dynamic facial expression database. Image and Vision Computing, 32(10), 692â706. 99 Appendices Appendix A: Demographics Questionnaire Please indicate your gender: o Male o Female o Other, please specify [blank text box] o Prefer not to answer 1Please select your age: o 17-20 o 21-25 o 26-30 o 31-35 o 36-40 o 41-45 o 46-50 o 51+ o Prefer not to answer Please indicate your race/ethnicity (Black, Latino/Latina, Asian, White, Other [if other, indicate in the text box which race/ethnicity]/Prefer not to answer): [blank text box] 2Please indicate your year of study: [blank text box] o 3rd year o 4th year o Residency Training [please indicate year] [blank text box] o Other [if other, please specify in the text box] 2Please indicate one residency training program you are interested in or currently taking (e.g., Family Medicine, General Surgery). [blank text box] 1 In Study 1, participants were asked to provide their age in years using free-response format. In Study 2, the response format was revised to multiple choice with age ranges (as shown) to ensure residents could not be easily identified by age as recommended by the UBCO BREB. 2 These questions were only asked for medical residents in Study 2. 100 Appendix B: Interpersonal Reactivity Index (IRI) The following statements inquire about your thoughts and feelings in a variety of situations. For each item, indicate how well it describes you by choosing the appropriate letter on the scale at the top of the page: A, B, C, D, or E. When you have decided on your answer, fill in the letter on the answer sheet next to the item number. READ EACH ITEM CAREFULLY BEFORE RESPONDING. Answer as honestly as you can. Thank you. ANSWER SCALE: A B C D E DOES NOT DESCRIBES ME DESCRIBE ME VERY WELL WELL 1. I daydream and fantasize, with some regularity, about things that might happen to me. ____ 2. I often have tender, concerned feelings for people less fortunate than me. ____ 3. I sometimes find it difficult to see things from the "other guy's" point of view. ____ 4. Sometimes I don't feel very sorry for other people when they are having problems. ____ 5. I really get involved with the feelings of the characters in a novel. ____ 6. In emergency situations, I feel apprehensive and ill-at-ease. ____ 7. I am usually objective when I watch a movie or play, and I don't often get completely caught up in it. ____ 8. I try to look at everybody's side of a disagreement before I make a decision. ____ 9. When I see someone being taken advantage of, I feel kind of protective towards them. ____ 10. I sometimes feel helpless when I am in the middle of a very emotional situation. ____ 11. I sometimes try to understand my friends better by imagining how things look from their perspective. ____ 12. Becoming extremely involved in a good book or movie is somewhat rare for me. ____ 13. When I see someone get hurt, I tend to remain calm. ____ 14. Other people's misfortunes do not usually disturb me a great deal. ____ 15. If I'm sure I'm right about something, I don't waste much time listening to other people's arguments. ____ 16. After seeing a play or movie, I have felt as though I were one of the characters. ____ 17. Being in a tense emotional situation scares me. ____ 18. When I see someone being treated unfairly, I sometimes don't feel very much pity for them. ____ 19. I am usually pretty effective in dealing with emergencies. ____ 20. I am often quite touched by things that I see happen. ____ 21. I believe that there are two sides to every question and try to look at them both. ____ 22. I would describe myself as a pretty soft-hearted person. ____ 23. When I watch a good movie, I can very easily put myself in the place of a leading character. ____ 24. I tend to lose control during emergencies. ____ 25. When I'm upset at someone, I usually try to "put myself in his shoes" for a while. ____ 26. When I am reading an interesting story or novel, I imagine how I would feel if the events in the story were happening to me. ____ 27. When I see someone who badly needs help in an emergency, I go to pieces. ____ 28. Before criticizing somebody, I try to imagine how I would feel if I were in their place. ____ 101 Appendix C: Emotion Regulation Questionnaire (ERQ) We would like to ask you some questions about your emotional life, in particular, how you control (that is, regulate and manage) your emotions. The questions below involve two distinct aspects of your emotional life. One is your emotional experience, or what you feel like inside. The other is your emotional expression, or how you show your emotions in the way you talk, gesture, or behave. Although some of the following questions may seem similar to one another, they differ in important ways. For each item, please answer using the following scale: 1.____ When I want to feel more positive emotion (such as joy or amusement), I change what Iâm thinking about. 2.____ I keep my emotions to myself. 3.____ When I want to feel less negative emotion (such as sadness or anger), I change what Iâm thinking about. 4.____ When I am feeling positive emotions, I am careful not to express them 5.____ When Iâm faced with a stressful situation, I make myself think about it in a way that helps me stay calm. 6.____ I control my emotions by not expressing them. 7.____ When I want to feel more positive emotion, I change the way Iâm thinking about the situation. 8.____ I control my emotions by changing the way I think about the situation Iâm in. 9.____ When I am feeling negative emotions, I make sure not to express them. 10.____ When I want to feel less negative emotion, I change the way Iâm thinking about the situation. 1 2 3 4 5 6 7 strongly disagree neutral strongly agree 102 Appendix D: Emotion Regulation Questionnaire (ERQ) This test measures different personality characteristics- that is, the ways in which peopleâs personality styles make them different from each other. Read each statement carefully and decide how false or true it is as a description of you. Then, mark the best choice that corresponds to your answer on this form. Use the answer choices provided as follows: F = False(1), MF = Mostly False(2), MT = Mostly True(3), T = True(4). Even if you feel that a statement is neither false nor true about you, or if you are not sure which answer to choose, select the answer that is the closest to describing you. Try to be as honest as you can. Please be sure to give your own opinion about whether each statement is false or true about you. Remember, you have the right to leave any and/or all of the questions blank. 1 2 3 4 4. I have always seen myself as something of a rebel. F MF MT T 10. I am easily flustered in pressured situations. F MF MT T 12. I would find the job of a movie stunt person exciting. F MF MT T 18. A lot of people have tried to âstab me in the back.â F MF MT T 19. Peopleâs reactions to the things I do often are not what F MF MT T I would expect. 22. Iâm not good at getting people to do favors for me. F MF MT T 27. A lot of times, I worry when a friend is having F MF MT T personal problems. 32. I donât let everyday hassles get on my nerves. F MF MT T 33. I could be a good âcon artist.â F MF MT T 34. I have a talent for getting people to talk to me. F MF MT T 36. I might like to travel around the country with some F MF MT T motorcyclists and cause trouble. 40. When Iâm with people who do something wrong, I F MF MT T usually get the blame. 46. I feel sure of myself when Iâm around other people. F MF MT T 47. Parachute jumping would really scare me. F MF MT T 58. I like to dress differently from other people. F MF MT 103 T 67. I enjoy seeing someone I donât like get into trouble. F MF MT T 75. It bothers me a lot when I see someone crying. F MF MT T 76. I get stressed out when Iâm âjugglingâ too many tasks. F MF MT T 77. I like to (or would like to) wear expensive and âshowyâ clothing. F MF MT T 80. I donât care about following the ârulesâ; I make my own rules F MF MT T as I go along. 84. Iâve been the victim of a lot of bad luck. F MF MT T 87. Iâm hardly ever the âlife of the party.â F MF MT T 89. Iâve thought a lot about my long-term career goals. F MF MT T 97. I feel bad about myself after I tell a lie. F MF MT T 108. I push myself as hard as I can when Iâm working. F MF MT T 109. I get very upset when I see photographs of starving people. F MF MT T 113. I hardly ever end up being the leader of a group. F MF MT T 115. I might like flying across the ocean in a hot-air balloon. F MF MT T 119. I worry about things even when thereâs no reason to. F MF MT T 121. When I am doing something important, like taking F MF MT T a test or doing my taxes, I check it over first. 122. People I thought were my âfriendsâ have gotten me into trouble. F MF MT T 130. I think long and hard before I make big decisions. F MF MT T 136. I quickly get annoyed with people who do not give me what F MF MT T I want. 137. If I were a firefighter, I would like the thrill of saving F MF MT T someone from the top of a burning building. 140. I can remain calm in situations that would make many other F MF MT T people panic. 104 145. I watch my finances closely. F MF MT T 148. I am a daredevil. F MF MT T 149. I would like to hitchhike across the country with no plans. F MF MT T 153. I often place my friendsâ needs above my own. F MF MT T 154. If I canât change the rules, I try to get others to bend them for me F MF MT T
Thesis/Dissertation
eng
Psychology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Defining the line : examining the role of empathy in the prosocial treatment of pain
Text