Arts and Social Sciences, Irving K. Barber Faculty of (Okanagan)
Psychology, Department of (Okanagan)
DSpace
UBCO
Rootman, Joseph Marc
Doctor of Philosophy - PhD
University of British Columbia
Heavy episodic drinking, defined as three drinks consumed on a single occasion in females and four drinks in males, is a prevalent and dangerous behaviour. It is becoming increasingly common for cannabis to be co-used with alcohol such that the psychoactive effects of the two substances overlap. Research has indicated that the association between cannabis-alcohol co-use and short-term harms may be complicated by third variable factors such as number of drinks, but further research is needed to understand these complexities. Additionally, little is known about how co-use relates to other short-term outcomes beyond harms such as injury or embarrassment. The present study sought to explore the mechanisms by which co-use relates to harm through indirect pathways of number of drinks and psychoactive effects. Furthermore, co-use was investigated in relation to next-day regret and achievement of intended outcomes of cannabis and alcohol use. Ecological momentary assessment methodology was used to collect observations longitudinally among young adults in Canada. At the end of a drinking event, participants completed a validated measure to assess alcohol related stimulation and sedation psychoactive effects. The following morning, participants reported on harms, co-use with cannabis, drinks, regret, and intention fulfilment. Co-use was found to be moderated by number of drinks such that co-use was increasingly protective of harms as additional drinks were consumed. Moreover, co-use was associated with significantly less regret relative to when alcohol was consumed without cannabis. Finally, among participants that reported motivation to engage in cannabis- or alcohol-use for the purposes of enhancement, coping, and socializing, co-use was found to better achieve these outcomes compared to alcohol-use alone. Together, these findings indicate that co-use is no more harmful than alcohol-use alone, and at extreme levels of drinking is associated with protective outcomes. Contemporary harm reduction guidelines are designed to discourage cannabis-alcohol co-use with the intention of reducing harm. Findings suggest that empirically informed approaches should focus reduction strategies on drinks, rather than co-use. Future research should aim to investigate potential protective effects of co-use to determine contexts where co-use may be harm-reducing.
CANNABIS-ALCOHOL CO-USE PROTECTS FROM DRINKING REGRET WITHOUT INCREASING HARMS by Joseph Marc Rootman BA (hons)., University of British Columbia, 2017 M.A., University of British Columbia, 2020 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) July 2024 © Joseph Marc Rootman, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a dissertation entitled: CANNABIS-ALCOHOL CO-USE PROTECTS FROM DRINKING REGRET WITHOUT INCREASING HARMS ______________________________________________________________________________ submitted by Joseph Marc Rootman in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Zach Walsh, Irving K. Barber Faculty of Arts and Social Sciences Supervisor Dr. Susan Holtzman, Irving K. Barber Faculty of Arts and Social Sciences Supervisory Committee Member Dr. Michael-John Milloy, The University of British Columbia (Vancouver) Supervisory Committee Member Dr. Rachelle Hole, Faculty of Health and Social Development University Examiner Dr. Bernie Pauly, University of Victoria External Examiner iiiAbstract Heavy episodic drinking, defined as three drinks consumed on a single occasion in females and four drinks in males, is a prevalent and dangerous behaviour. It is becoming increasingly common for cannabis to be co-used with alcohol such that the psychoactive effects of the two substances overlap. Research has indicated that the association between cannabis-alcohol co-use and short-term harms may be complicated by third variable factors such as number of drinks, but further research is needed to understand these complexities. Additionally, little is known about how co-use relates to other short-term outcomes beyond harms such as injury or embarrassment. The present study sought to explore the mechanisms by which co-use relates to harm through indirect pathways of number of drinks and psychoactive effects. Furthermore, co-use was investigated in relation to next-day regret and achievement of intended outcomes of cannabis and alcohol use. Ecological momentary assessment methodology was used to collect observations longitudinally among young adults in Canada. At the end of a drinking event, participants completed a validated measure to assess alcohol related stimulation and sedation psychoactive effects. The following morning, participants reported on harms, co-use with cannabis, drinks, regret, and intention fulfilment. Co-use was found to be moderated by number of drinks such that co-use was increasingly protective of harms as additional drinks were consumed. Moreover, co-use was associated with significantly less regret relative to when alcohol was consumed without cannabis. Finally, among participants that reported motivation to engage in cannabis- or alcohol-use for the purposes of enhancement, coping, and socializing, co-use was found to better achieve these outcomes compared to alcohol-use alone. Together, these findings indicate that co-use is no more harmful than alcohol-use alone, and at extreme levels of drinking is associated with protective outcomes. Contemporary harm reduction guidelines are ivdesigned to discourage cannabis-alcohol co-use with the intention of reducing harm. Findings suggest that empirically informed approaches should focus reduction strategies on drinks, rather than co-use. Future research should aim to investigate potential protective effects of co-use to determine contexts where co-use may be harm-reducing. vLay Summary Heavy episodic drinking, defined as three drinks consumed on a single occasion in females and four drinks in males, is a prevalent and dangerous behaviour. Cannabis is commonly co-used with alcohol wherein the two substances are used in a short enough time frame to allow for overlapping effects. This study examined the relationship between co-use to three outcomes: harms, regret, and the extent to which initial intentions of the substance use event were fulfilled. Co-use protected from experiencing alcohol harms, such as injury, within the context of extreme levels of drinking (10 or more drinks). Co-use events were also less likely to be linked to feelings of regret afterwards. Finally, co-use was more likely to fulfil initial intentions for substance use within the domains of enhancement, coping, and socializing. Together, these findings indicate that co-use can mitigate harms and promote positive outcomes, especially at higher levels of drinking. viPreface Joseph Marc Rootman was the primary contributor to the work presented in this dissertation, and was responsible for study design, data analysis, and writing of the manuscript. Generative artificial intelligence was not use in any aspects of this research including drafting and preparation of the dissertation. The University of British Columbia's Okanagan Campus Behavioral Research Ethics Board granted approval to this study H22-00805. viiTable of Contents Abstract ......................................................................................................................................................................... iii Lay Summary..................................................................................................................................................................v Preface .......................................................................................................................................................................... vi Table of Contents ........................................................................................................................................................ vii List of Tables ................................................................................................................................................................ ix List of Figures ............................................................................................................................................................... x Acknowledgements ....................................................................................................................................................... xi Dedication .................................................................................................................................................................... xii CHAPTER 1: Introduction ........................................................................................................................................... 1 Heavy Episodic Drinking ........................................................................................................................................ 2 Cannabis-Alcohol Co-Use ....................................................................................................................................... 3 CU and HED: Compliment or Substitute ............................................................................................................. 8 Daily-Level Research ......................................................................................................................................... 11 Psychoactive Effects of Cannabis-Alcohol Co-Use ............................................................................................. 15 Psychoactive Effects of Alcohol Use .................................................................................................................. 16 Psychoactive Effects of Cannabis Use ............................................................................................................... 20 Direct Investigations into the Psychoactive effects of Cannabis-Alcohol Co-Use............................................. 22 Regret ................................................................................................................................................................. 25 Intention Fulfilment ............................................................................................................................................ 28 Summary ................................................................................................................................................................ 29 Purpose of the Present Study .............................................................................................................................. 34 CHAPTER 2: Methods................................................................................................................................................ 38 Procedure ............................................................................................................................................................... 38 Measures................................................................................................................................................................. 40 Harms ................................................................................................................................................................. 40 Co-Use ................................................................................................................................................................ 41 Tendency to Co-Use ........................................................................................................................................... 41 Number of drinks ................................................................................................................................................ 41 Typical Number of drinks ................................................................................................................................... 42 Sedation and Stimulation .................................................................................................................................... 42 Typical sedation and stimulation........................................................................................................................ 43 Regret About Cannabis and Alcohol Use ........................................................................................................... 43 Typical Motivation.............................................................................................................................................. 43 Daily Motivation ................................................................................................................................................. 44 Intention Fulfilment ............................................................................................................................................ 44 Analytical Plan ....................................................................................................................................................... 45 Preliminary Analyses .......................................................................................................................................... 45 Multi-Level Modeling ......................................................................................................................................... 46 Data Preparation................................................................................................................................................ 46 Primary analysis ................................................................................................................................................. 47 CHAPTER 3: Results .................................................................................................................................................. 51 Participants ............................................................................................................................................................ 51 Multilevel Modeling .............................................................................................................................................. 52 Aim 1: Relationship between co-use and harm without controlling for number of drinks. ............................... 52 viiiAim 2: Moderation of co-use and harm by number of drinks. ........................................................................... 53 Aim 3: Co-use predicting number of drinks. ...................................................................................................... 55 Aim 4: Mediation of the relationship between co-use and harms via number of drinks. ................................... 56 Aim 5: Mediation of the relationship between co-use and harms via AU-sedation. .......................................... 59 Aim 6: Mediation of the relationship between co-use and harms via AU-stimulation. ..................................... 59 Aim 7: Co-Use Predicting Regret ....................................................................................................................... 61 Secondary Aim: Co-use Predicting Intention Fulfilment ................................................................................... 62 CHAPTER 4: Discussion ............................................................................................................................................ 65 Co-Use Relationship to Harm .............................................................................................................................. 65 Heavy Episodic Drinking ...................................................................................................................................... 66 Light Drinking Events .......................................................................................................................................... 69 Co-use Relationship to Regret .............................................................................................................................. 71 Co-use Relationship to Intention Fulfilment....................................................................................................... 73 Implications ............................................................................................................................................................ 74 Strengths, Limitations, and Future Directions ................................................................................................... 76 Conclusion .............................................................................................................................................................. 79 References.................................................................................................................................................................... 81 Appendices ................................................................................................................................................................... 96 Appendix A ............................................................................................................................................................ 96 Appendix B ........................................................................................................................................................... 100 Appendix C .......................................................................................................................................................... 105 Appendix D .......................................................................................................................................................... 106 Appendix E ........................................................................................................................................................... 108 Appendix F ........................................................................................................................................................... 109 ixList of Tables Table 1 Descriptive of Drinking and Co-Use Behaviour…………………………….. 52 Table 2 Co-Use Predicting Number of drinks………………………………………... 57 Table 3 Models for Number of drinks Mediating the Relationship Between Co-Use and Harms……………………………………………………………………………. 58 Table 4 Number of drinks as a Mediator in the Relationship Between Co-Use and Harms across light drinking and heavy episodic drinking events…………………… 59 Table 5 Sedation as a Mediator in the Relationship Between Co-Use and Harms….. 60 Table 6 Stimulation as a Mediator in the Relationship Between Co-Use and Harms.. 61 Table 7 Co-Use Predicting Intention Fulfilment…………………………………….. 63 xList of Figures Figure 1 The Proposed Relationship Between Co-Use and Harms via Number of drinks…………………………………………………………………………………. 33 Figure 2 Proposed Relationship Between Co-Use and Harms via Alcohol Use Sedation. …………………………………………………………………………… 33 Figure 3 Proposed Relationship Between Co-Use and Harms via Alcohol Use Stimulation…………………………………………………………………………… 34 Figure 4 Number of Drinking or Co-Use Observations Provided Across Participants by Gender. …………………………………………………………………………… 51 Figure 5 Interaction between Co-use and Number of Drinks onto Harms…………… 54 Figure 6 Interaction between Co-use and Number of Drinks onto Harms…………… 55 Figure 7 Relationship Between Co-Use and Number of Drinks Across Light and Heavy Drinking Events………………………………………………………………. 56 Figure 8 Interaction between Co-use and Number of Drinks onto Daily Social Intention Fulfilment………………………………………………………………….. 64 xiAcknowledgements I would like to acknowledge that the grounds on which this dissertation project was completed, at the University of British Columbia – Okanagan, are situated on the unceded territory of the Sylix Okanagan Nation. Over the past decade, the mentorship I have received from Dr. Zach Walsh has been instrumental in my journey to the clinical scientist I am today. Most students are lucky to find a supervisor that treats them with the respect and devotion that I have received in my training. I am deeply grateful that, more than just a supervisor, I had a wizard to walk alongside me on this path. What a long and strange trip it has been. I would like to acknowledge the support of my committee members. Throughout both my master’s and doctoral training, Dr. M-J Milloy and Dr. Susan Holtzman have lent wisdom, positivity, and care into our work together. It is through the synergy that came from the gathering of these amazing humans, and my wizard supervisor, that have shaped this document into something that will remain one of my greatest lifetime accomplishments. I am incredibly proud of the work that we have done. A thank you to my friends and family for your unwavering support. Even when distance and pandemic lockdowns kept us separate, I have always felt loved. I could not have kept moving, as I did, without that wind beneath my sails. Finally, I would not be here in Canada, free to publish dissertations about cannabis, if not for the determination of my ancestors to find a home they could live free of persecution. Zaida Moskowitz survived Auschwitz to come to Canada and earn his PhD (Plumbing, Heating, and Draining), and I know he would be incredibly proud to see me with mine. xiiDedication Dedicated to Dr. Megumi Iyar. With you by my side: the paths were illuminated even when dark, the batons were weightless even when made of stone, and the moments unforgettable, even if not remembered. Every single day is a new type of magic with you. “Everybody dun know di program” -Flowdan 1 CHAPTER 1: Introduction Alcohol is the most commonly used substance in Canada, with more than three quarters of Canadian adults reporting use at least once in the past year (Canadian Center for Substance Abuse [CCSA], 2017). While alcohol is widely accepted in Canadian society, the health costs associated with heavy drinking presents a considerable burden to the welfare of all Canadians (Brown et al., 2021; CCSA, 2017). Alcohol and cannabis are frequently used in combination in a short enough timeframe to allow for overlapping psychoactive effects (Barrett et al., 2006; Shillington, & Clapp, 2006; White et al., 2019), and this behaviour has been characterized as harmful by health authorities (Canadian Nurses Association, 2017; Cochrane Canada, 2021; Delphi Behavioral Health Group, 2019; HealthLinkBC, 2019). Short-term harms in this context refers to negative consequences of AU that occur during or immediately after the drinking event, for example injury, social embarrassment, and hangovers. Past research has failed to consistently determine whether cannabis use (CU) in the context of a drinking event serves to inhibit or exacerbate the substantial short-term harms associated with alcohol use (AU; Lee et al., 2020; Linden-Carmichael, 2020; Lipperman-Kreda, 2017; Mallet et al., 2019). Additionally, little is known about how co-use relates to other outcomes such as regret and achievement of intended reason for substance use. Extant literature suggests that cannabis-alcohol co-use may serve to impact the harm of AU through number of drinks, alcohol sedation, and alcohol stimulation. First, co-using cannabis and alcohol may influence the number of drinks that are consumed within the context of a discrete drinking event. Given the known associations between number of drinks and harms (Taylor et al., 2010), it is likely that reductions in number of drinks would indirectly mitigate alcohol related harms. Second, co-use may increase the sedating psychoactive effects associated 2with drinking. Alcohol psychoactive effects can be categorized as either sedating or stimulating (Martin et al., 1993) with sedating effects typically described as more negative in valence, for example feeling “down”. Sedating psychoactive effects have been shown to be negatively associated with alcohol related harms (Leeman et al., 2008; Morgenstern; 2012; Murphy, 2021). As such, it may be that CU increases AU sedation which therein protects from alcohol related harms. A third mechanism that could explain how co-use relates to harm is through the stimulating psychoactive effects of AU. In contrast to sedation, the stimulating psychoactive effects of alcohol, such as gregariousness, (Morris et al., 2016) have been linked to increases in alcohol related harm (Leeman et al., 2008; Morgenstern; 2012; Murphy, 2021). Given that AU stimulation increases risk, it is possible that co-use lowers AU stimulation thereby reducing harms. Together, number of drinks, sedation, and stimulation represent three possible mechanisms in which co-use could protect from alcohol related harms. Heavy Episodic Drinking Heavy Episodic drinking (HED) is a public health concern. The Canadian Low-Risk Drinking Guidelines define HED as 4 or more drinks consumed per occasion for females, and 5 or more drinks consumed in any given occasion for males (CCSA, 2023). Much of the health burden associated with AU can be attributed to HED. For example, in Canada, HED directly contributes to 77,000 annual hospitalizations, which exceeds those attributable to heart attacks (CCSA 2017). Further, the majority of alcohol-related motor vehicle crash fatalities are caused by drivers engaging in HED (Brown et al., 2021), which highlights how the harms associated with HED threaten even those that are not engaging in the behaviour. These, and other, alcohol-related harms are estimated to cost the Canadian taxpayer $455 annually, totaling 16.6 billion dollars across the nation (Canadian Substance Use Costs and Harms Scientific Working Group, 32020). Despite these dangers associated with HED, 16.6% of Canadians reported at least one HED session per month in 2021 (Statscan, 2021a), and recent surveys suggest that rates of HED have increased through the Covid-19 pandemic (Statscan, 2021b). Heavy drinkers experience both acute and chronic harms associated with their AU. Acutely, AU related harms, such as injury and motor vehicle collisions, increase dramatically with each drink consumed (Taylor et al., 2010). Additionally, individuals that repeatedly engage in HED are at risk for negative long-term physical and psychological health outcomes (Briasoulis, Agarwal, & Messerli, 2012; Rehm et al., 2010; Stavro, Pelletier, & Potvin, 2013). Young adults are at highest risk for alcohol-related harms, with one third reporting regular engagement in HED (CCSA, 2017). Additionally, young adults tend to engage in HED at higher levels of number of drinks and at a higher frequency than other groups (CCSA, 2017). Academic achievement among college students is particularly susceptible to the impacts of heavy drinking such that both HED frequency and intensity have been linked to reduced academic achievement in a number of studies (Ansari et al., 2020; Mota et al, 2009; Porter & Pryor, 2007). These findings characterize HED as a prominent and hazardous behaviour among young adults that becomes more threatening as heavy drinking sessions increase in frequency or intensity. As such, the identification of factors that influence number of drinks within the context of a discrete AU session is essential to the development of effective harm-reduction strategies targeted at the mitigation of HED related negative outcomes. Cannabis-Alcohol Co-Use One in four young adults report engaging in both cannabis and alcohol use (Shillington & Clapp, 2006). Many of these cannabis and alcohol using students report co-use of the substances in a manner wherein use of one substance occurs soon after another which allows for 4overlapping psychoactive effects. Indeed, co-use is near ubiquitous among this population such that 95% of cannabis and alcohol using young adults report lifetime co-use of the substances (Barrett et al., 2006). Roughly 15% of the Canadian population reports monthly or more frequent co-use, and the prevalence appears to be rising (Hobin, et al., 2023). With respect to frequency, cannabis-alcohol co-users report engaging in the practice, on average, twice monthly (White et al., 2019). In sum, cannabis-alcohol co-use is prevalent among Canadian young adults. Relative to either substance on its own, many questions have not been answered with respect to the relationship between cannabis-alcohol co-use and negative outcomes. A number of studies have identified that individuals who engage in cannabis-alcohol co-use are at a higher risk for alcohol related harms (Green et al., 2016, Magill et al., 2009; Shillington, & Clapp, 2001; Shillington, & Clapp, 2006), cannabis related harms (Looby et al., 2021), intoxicated driving (Terry-McElrath et al., 2013), negative social outcomes (Subbaraman & Kerr, 2015), and mental health concerns (Midanik et al., 2007; Pacek et al., 2012). In line with these findings, harm-reduction guidelines and policies often label co-use to be more dangerous than AU-alone with the intention of mitigating negative outcomes such as impaired driving and alcohol or cannabis use related poising (Canadian Nurses Association, 2017; CCSA, 2020; Delphi Behavioral Health Group, 2019; HealthLinkBC, 2019). These guidelines may be erroneously applying between-person findings, which suggest that co-users are at a higher risk for negative outcomes, to a within-person context: whether or not cannabis should be added to a specific session of drinking. Extrapolating within-person conclusions about co-use from between-person findings is problematic. Specifically, the number of drinks associated with co-use may be, in part, attributable to risk factors shared among co-users, such as the tendency to engage in heavier 5substance use (Yurasek et al., 2017), rather than a causal effect of co-use per se. Indeed, both co-use in itself, and many of the outcomes related to co-use, such as impaired driving and problematic social behaviours (Subbaraman & Kerr, 2015; Terry-McElrath et al., 2013), are closely linked with trait factors such as behavioural undercontrol (Treloar et al., 2012). Moreover, individuals that tend to engage in these impulsive behaviours have been identified as prone to disinhibition in general (Hicks et al., 2004). This suggests that some of the previously identified associations between co-use and outcomes partly reflect individual differences such as trait levels of behavioural control among co-users. This pathway, which highlights contributions of individual differences in the association to harm among co-users, contrasts the causal pathway implied by harm reduction strategies whereby engaging in co-use increases the likelihood of harm relative to when alcohol is used in isolation. Specifically, it is not clear whether it is the use of cannabis in a night of drinking, or the characteristics of people that tend to co-use, that are driving associations between cannabis-alcohol co-use and negative outcomes. Taken together, guidelines and policy which intend to guide behaviours that vary within individuals, such as whether cannabis should be combined with alcohol on any given night, are best informed by within-person research that is not confounded by shared risk-factors and other individual characteristics. One way to separate between- and within-person effects is through within-person research design. With multiple observations per individual, it is possible to compare differences across observations within any given individual. For example, the number of harms experienced can be compared across events wherein co-use occurred relative to alcohol alone. Thus, in a study where each individual contributes one co-use observation and one AU alone observation, the difference between these two observations can be compared. Within-person effects in this 6case would then represent the average variation that occurred across participants when co-use occurred relative to when alcohol was used alone. Through this measurement of within-person variation, it is possible to determine how much change in harm occurs when individuals decide to co-use, regardless of their between-person characteristics such as a tendency to drink heavily. In this way, these studies can inform how much the decision to co-use impacts harm, whereas cross-sectional observations can only speak to the effects of being the type of person that tends to co-use. This is an important distinction because between-person findings have noted associations between people that co-use and several trait level risk factors for harm such as impulsivity, risk taking, and increased substance use behaviour (Yurasek et al., 2017). These risk factors confound between-person research of co-use such that it is unclear whether it is the behaviour of co-use that drives harm, or rather, that the type of person who chooses to co-use tends to also be more impulsive and drawn to risk. Taken together, within-person findings are better suited to guide guidelines and policy related co-use because they are not confounded by between-person characteristics that vary across individuals that co-use. Compared to between-person research, within-person findings relating to cannabis-alcohol co-use are less clear in associating co-use with number of drinks. In the context of a HED session, one study noted that co-use did not increase the risk of number of drinks beyond the risk associated with HED in the absence of CU (Mallet et al., 2019). Moreover, AU alone, CU alone and non-HED cannabis-alcohol co-use events were similar in their associations to negative outcomes, and all of these were noted as significantly less harmful than both HED co-use events and AU only HED events. Similarly, when number of drinks is controlled for, some studies have noted that co-use events present no additional risk of experiencing harms among both young adults (Lee et al., 2020) and adolescents (Lipperman-Kreda, 2017). In contrast, one 7study identified higher levels of harms associated with co-use even after controlling for number of drinks (Linden-Carmichael, 2020), whereas others have noted reduced number of harms associated with co-use at higher levels of drinking (Sokolovsky et al., 2020). Given that this growing body of within-person research has largely suggested AU to be the driver of consequences in HED events, with one study highlighting potentially harm-reducing effects of co-use relative to AU alone, strict guidelines and regulations developed with the intention of reducing co-use are worthy of reconsideration. Past literature has highlighted number of drinks to be closely linked to both co-use and harm, which suggests the importance of drinks in understanding the safety profile co-use. Number of drinks may influence the relationship between co-use and harm through either moderation, mediation, or both. First, co-use may be moderated by number of drinks in its association to harm such that the influence of co-use is inconsistent across levels of drinking. Given that harms increase exponentially with each additional drink, it follows that any protective or harmful effects of co-use would be most salient at high levels of drinking where harms are most prominent. This helps to explain findings that suggest a protective effect of co-use that emerges at higher levels of drinking (Sokolovsky et al., 2020). A second way that number of drinks may influence harm is through an indirect mediation pathway wherein co-use is associated with changes to number of drinks which therein impacts harm. This pathway would also likely be most salient at higher levels of drinking wherein there is more room for variation in both number of drinks and harms. Given these potential moderated and mediated effects, it is critical that the relationship between co-use and number of drinks is considered when characterizing co-use and its association to harm. 8CU and HED: Compliment or Substitute Behavioural economics theory can be useful in facilitating our understanding of the impact which cannabis-alcohol co-use can have on number of drinks. Specifically, when applied to human behaviour, economic principles, such as supply and demand, can illustrate how one behaviour can either increase, decrease or have no impact on the likelihood a different behaviour will occur (Hursh et al., 2015). Within the context of substance use, substitution effects describe cases where the use of one substance decreases the use of another. Substitution effects may occur when the primary motivations for an individual engaging in substance use can be met through the use of either substance, thus when one is used, demand for the other decreases. For example, methadone replacement therapy seeks to effectively reduce opioid use by providing patients with methadone, which can mitigate cravings and withdrawal symptoms associated with opioid cessation. As such, individuals that are primarily motivated towards opioid use to attenuate negative psychological and physiological cessation symptoms, may be able to meet these motivations with the use of methadone, thus enabling them to reduce their use of other, more harmful, substances such as fentanyl (Fareed, 2010). In contrast, compliment effects describe cases where the use of one substance increases the use of another, which may occur when the two substances act synergistically to increase the pleasurable effects of either substance alone. For example, when consuming alcohol, tobacco users report increased pleasure and decreased punishment after smoking a cigarette. This synergy promotes the complimentary use of these two substances whereby alcohol increases cigarette use (Piasecki et al., 2011). Given the established link between AU number of drinks and negative outcomes (Taylor et al., 2010; Piazza-Gardner, 2016) the elucidation of any complimentary or substitutive 9effects within the context cannabis-alcohol co-use is necessary to inform whether this pattern of behaviour is harmful. Initial investigations into the influence that CU exerts on alcohol consumption date back to the 1970’s, with a single subject case study (Mikuriya, 1970). Here, a woman recovering from alcohol use disorder reported that she found CU to be an effective, and enjoyable substitute for alcohol. Indeed, many cannabis users report the substitution of cannabis for alcohol: several studies have noted that between 25 - 45% of medical cannabis users report that they intentionally use cannabis to replace their use of alcohol (Lucas et al., 2019; Lucas & Walsh, 2017; Reiman, 2009). In contrast to reports of effective substitution within the context of survey data, other approaches have been inconsistent in replicating substitution effects, and have noted compliment effects instead. One systematic review helps to summarize the large body of literature pertaining to cannabis-alcohol compliment and substitute effects (Risso et al., 2020). In line with previous reviews (Subbaraman, 2016), the authors found evidence in support of both substitution and compliment effects: 30 studies supported substitutions effects, 17 studies supported complimentary effects, and 14 did not find evidence for either. Based on these equivocal findings, the authors suggested that the extent to which cannabis inhibits or exacerbates number of drinks likely lies on a spectrum wherein moderators, such as sample characteristics and study design, influence the association between cannabis use and reduced number of drinks. For example, the findings of the 20 reviewed policy evaluation studies trended in support of substitution effects whereby liberalization of cannabis policy, such as the introduction of medical cannabis laws, has been followed by reductions in alcohol consumption at a population level. In contrast to the findings of these broad epidemiological investigations, 5 studies assessing CU and 10AU found evidence in favour of complimentary effects. In these studies, CU and AU were positively correlated suggesting that cannabis and alcohol use are mutually influential insomuch that the more an individual uses one, the more likely they are to engage in the use of the other. Of note is that 4 of these studies were conducted on adolescence and young adults below the age of 21, which may reflect a specific vulnerability to compliment effects among young people. Finally, the authors note that these effects may vary according to cannabis regulations, such that substitution effects are more likely in jurisdictions with liberalized cannabis regulations. These methodological, demographic, and contextual effect moderators fall in-line with those identified in previous systematic reviews (Schlienz and Lee, 2018; Subbaraman, 2016), and highlight the need for further, more fine grained, research to elucidate the complex nature of cannabis-alcohol substitution and compliment effects. Additional approaches to these broad epidemiological, population level, or cross-sectional methodologies may help to extend cannabis-alcohol substitution literature by uncovering complex cannabis-alcohol behaviour patterns as they unfold in the real world. As an example, one study of cannabis-alcohol substitution effects identified that there have been reductions in beer sales since the legalization of recreational cannabis in Canada (Mital et al., 2023). Nonetheless, this study is limited in its conclusions beyond noting the presence of a substitution effect such that key questions are left unanswered. For example, these findings leave open how CU is substituted for AU. One option is that CU impacts AU frequency such that Canadians have been engaging in fewer drinking sessions overall, whereas another explanation is that AU intensity is reduced via CU such that less drinks have been consumed in any given event. Alternatively, it is possible that some third variable, such as shifts in attitudes towards substance use, is linked to both legalization and changes in beer consumption. In order to fill 11these gaps in our understanding of cannabis-alcohol substitution, daily level data, which aim to capture behaviour within the context of discrete drinking events, is necessary. Daily-Level Research A variety of methodologies exist to gather daily-level data. One such methodology is the use of ecological momentary assessment (EMA) surveys to collect information on factors which are likely to vary from moment-to-moment within individuals, such as emotional states and substance use behaviours. Surveys are typically sent to participants multiple times a day with the expectation that they are to be completed at their earliest convenience. As such, EMA based methodologies have been particularly useful within the context of substance use in order to assess states and behaviours, such as number of drinks, that are known to be recalled with bias when assessed at later time-points (Gmel & Daeppen, 2007; Shiffman, 2009). Other daily-level methodologies which have found popular use in the substance use literature are daily diary or retrospective assessment tools. For example, the Timeline Follow-Back Technique (Sobell & Sobell, 1992) asks participants to recall on behaviours associated with an individual substance use event, such as how much an individual drank last Saturday, rather than patterns of behaviour in general, such as average levels of number of drinks across drinking events. A review of the literature confirms that daily-level examinations of cannabis-alcohol co-use represent a rapidly growing area of research. Specifically, since the first daily level examination published in 2014 (McKetin et al., 2014), 12 more studies have followed to date (Gunn et al., 2018; Gunn et al., 2019; Gunn et al., 2021; Lee et al., 2020; Linden-Carmichael, 2020; Mcketin et al., 2014; Metrik et al., 2018; O’hara et al., 2016; Roche et al., 2019; Rootman, 2020; Wadell et al., 2021). In line with the findings of macro-level cannabis-alcohol substitution and complimentary research (Risso, 2020; Subbaraman, 2016), daily-level studies have been 12inconsistent in identifying whether CU increases, or decreases AU in a given AU event. Indeed, while five studies have noted that co-use events tended to be associated with increased drinking relative to AU only events (Gunn et al., 2018; Gunn et al., 2019; Lee et al., 2020; Metrik et al., 2018; Roche et al., 2019) the findings of two studies suggest that CU was not associated with number of drinks (Linden-Carmichael, 2020; McKetin et al., 2014), and three studies have noted substitution effects (Gunn et al., 2021; Rootman, 2020; Wadell et al., 2021). Moreover, several of the studies that identified complimentary or neutral effects associated with co-use have noted these effects to be subject to moderation such that co-use was associated with reduced number of drinks when considering individual differences and contextual factors (Gunn et al., 2019; Metrik et al., 2018; O’Hara et al., 2016; Wadell et al., 2021). Taken together, equivocal findings in the daily-level research suggest that cannabis may act as either a compliment and a substitute depending on characteristics of the co-user and the co-use event in question. With respect to determinants of compliment or substitute effects associated with cannabis-alcohol co-use, a number of moderators have been identified. For one, a study of U.S. veterans (Gunn et al., 2019) found that CU was complimentary for alcohol among recreational cannabis users, but substitutive for those using cannabis medicinally. This substitution effect was strongest among medical cannabis users that reported intentionally replacing CU for AU, a finding which aligns with macro-level longitudinal surveys of medical cannabis users (Lucas et al., 2020), which suggests that individuals that co-use with the intention of reduced AU may be subject to the most potent CU substitution effects. Likewise, coping motivations for engaging in AU have been identified as a significant moderator in the relationship between CU and number of drinks such that individuals that were motivated towards AU as a means of coping were most likely to exhibit substitution effects (O’Hara et al., 2016). Thus, motives for engaging in CU and 13AU appear to play a key role in the presence and strength of substitution effects. These compliment and substitute effects have also been noted as unstable across individual characteristics. For example, trait levels of impulsivity were shown to moderate the influence of co-use on AU such that individuals with low or moderate levels of impulsivity drank less on co-use days, whereas their highly impulsive counterparts drank equivalent amounts on co-use and AU only days (Wadell et al., 2021). Additionally, two studies have noted that compliment effects are strongest among individuals that exhibit higher levels of alcohol use disorder (AUD; Gunn et al., 2018; Metrik et al., 2018). Moreover, one of these studies, which utilized the Timeline Follow-Back Technique to assess CU and AU among veterans, noted that individuals that were high on measures of cannabis use disorder (CUD) drank significantly less on days where cannabis was used relative to AU only days (Metrik et al., 2018). Taken together, whether cannabis is complimentary or substitutive to AU depends on between-person factors such as co-user personality and substance use related psychopathology. Beyond these between-person factors, ordering of CU with respect to AU has been noted as a within-person moderator of the relationship between CU and number of drinks. Specifically, results of two studies (Gunn et al., 2021; Rootman et al., 2021) suggest that co-use is associated with reduced number of drinks, but only when CU occurs prior to the cessation of drinking in any-given co-use event. The consideration of substance use ordering is limited to these two studies, thus drawing into question findings of compliment effects identified elsewhere (Gunn et al., 2018; Gunn et al., 2019; Lee et al., 2020; Metrik et al., 2018; Roche et al., 2019) wherein co-use was not delineated in this way. Moreover, given that cannabis users report engaging in CU as a means of mitigating the negative effects of HED, such as headaches and nausea (Price Wolf et al., 2018), it is possible that these CU-post-AU events tend to be more closely associated with 14heavy drinking than when CU is used before or during AU (CU-before/CU-during AU event). Thus, when substance use ordering is not taken into consideration, these CU-post-AU events may obscure substitution effects through their attribution to co-use events despite important differences between co-use events depending on temporal placement of CU and AU within a discrete drinking event. Previous cannabis-alcohol co-use studies should be interpreted in light of potentially confounding effects of substance use ordering, and, moreover, it is imperative that future studies be designed with this factor in mind. To summarize, cannabis-alcohol co-use may either exacerbate or mitigate the harms associated with HED depending on whether CU compliments or substitutes AU, though decades of research has been equivocal in its determination of these effects. New lines of evidence (Gunn et al., 2021; Rootman et al., 2021) suggest that substance use ordering is an important consideration in the operationalization of co-use and that this ordering factor may be the key to accurately identifying compliment and substitution effects associated with cannabis-alcohol co-use. Thus, future research that distinguishes co-use events on the basis of substance use ordering is warranted to further our understanding of cannabis-alcohol substitution and compliment effects. Implicit in the distinguishing of co-use events in this way, wherein CU-post-AU events are delineated from co-use events wherein CU occurred before or during an AU event, is the assumption that overlapping psychoactive effects are relevant to co-use outcomes. This assumption is likely well-founded given that co-use is repeatedly defined in the literature as the use of two substances such that the effects overlap (Yurasek et al., 2017). Despite the inherent relationship between cannabis-alcohol co-use and the psychoactive effects associated with the practice, little is known about the extent to which these psychoactive effects contribute to AU 15related harms. Indeed, these effects may help to explain findings which suggest that co-use is associated with reduced harm even after controlling for AU at higher levels of number of drinks (Sokolovsky et al., 2020). Specifically, it may be that the psychoactive effects of co-use promote safety and mitigate harm relative to the effects associated with AU alone, thus even when drinks were held equal, co-use events were associated with fewer of harms associated with AU such as intoxicated driving and social embarrassment. If the psychoactive effects of co-use are associated with fewer consequences than AU alone, even beyond any potential impact of CU on number of drinks in an AU session, it would suggest a distinct mechanism by which co-use reduces harm associated with AU through alterations in the AU psychoactive experiences following co-use. Taken together, psychoactive effects are inherent to the definition of cannabis-alcohol co-use, and may help to understand the impact that co-use has on the dangers of AU. Psychoactive Effects of Cannabis-Alcohol Co-Use The salient commonality across all recreationally used substances is the ability to influence and alter one’s conscious experience. Psychoactive effects are known to vary dramatically in strength and qualitative experience both within each specific substance and across substances (e.g., Baylen & Rosenberg, 2006; Fischman et al., 1976; Kalman, 2002; Schmid et al., 2021; Yaden et al., 2020). These effects range from a mild buzz associated with light nicotine use (Kalman, 2002) to mystical experiences associated with a large doses of psychedelic drugs (Yaden & Griffiths, 2020). In line with this, psychoactive effects are diverse with respect to their influence on use related harms. For example, the disinhibiting effects of alcohol is suggested to be a primary contributor to alcohol-facilitated intimate partner violence (Eckhardt, 2015). In contrast, the pro-social, empathogenic, psychoactive effects associated with MDMA use are typically thought to be safe (Bershad, 2016; Yaden & Griffiths, 2020) and, 16further, are an integral mechanistic component of MDMA assisted psychotherapy in the treatment of post-traumatic stress disorder (Wagner et al., 2017). These examples highlight the centrality of psychoactive effects in understanding harms associated with the use of any substance, and in the case of MDMA, demonstrate how a deep understanding of psychoactive effects can be used to promote substance-use related benefits. When put into the context of cannabis-alcohol co-use, the nature of psychoactive effects that emerge when cannabis and alcohol are co-used may contribute to the extent to which CU increases or decreases alcohol consequences. Taken together, our appreciation for the potential of cannabis-alcohol co-use to reduce harm is likely incomplete without an understanding of the subjective experience associated with the practice. A relatively large body of literature exists on the psychoactive effects of AU, and, to a lesser degree, CU. Moreover, terms like “cross-faded”, which have been coined colloquially to describe the effects of cannabis alcohol co-use (Patrick & Lee, 2018), suggest that the experience of cannabis-alcohol co-use may be characterized as the crossing over of effects associated with AU and CU. As such, examining the psychoactive effects associated with either CU or AU in isolation may help to elucidate the qualitative nature of cannabis-alcohol co-use. Psychoactive Effects of Alcohol Use A review of validated measurement tools of AU psychoactive effects can help to describe the quality of AU effects. Three scales in particular have seen use in the literature: The Subjective High Assessment Scale-7 (SHAS; Schuckit et al., 2000), The Biphasic Alcohol Response Scale (BAES; Martin et al., 1993), and the Subjective Effects of Alcohol Scale (SEAS; Morean et al., 2014). All three of these scales tap into a number of potent sedative effects associated with AU, such as feeling “high”, “confused”, and “slow-thoughts”, while the BAES 17and SEAS, which are the two measures primarily used in modern literature, additionally consider a number of stimulating effects of AU such as feeling “energized” and “talkative”. These two sides of AU psychoactive effects have also been described as the rewarding and aversive psychoactive effects of AU (Morris et al., 2016) since the stimulating effects typically have a positive valence (e.g., elation) whereas the sedating effects generally have a negative valence (e.g., sluggishness). The notion that the subjective experience of AU is characterized by predominantly stimulating and sedating effects has been outlined within the biphasic model of alcohol response, which suggests that drinkers tend to experience the stimulating effects of AU in the earlier portions of a drinking event, but the sedating effects as blood alcohol content levels begins to decrease (Martin et al., 1993). This biphasic model of AU psychoactive effects is well established in the literature: a substantial number of studies have incorporated it into their investigations, and, further, both factor- and meta-analytic studies have provided significant support in favour of this effect profile (Ray et al., 2009; Quinn and Fromme, 2011). Taken together, contemporary estimations of AU psychoactive effects suggest that AU is characterized primarily by both stimulating and sedating effects that tend to be associated with early- and later-periods in a drinking event respectively. Psychoactive effects have been central to investigations of AU related harms for decades. This body of literature has cumulated into a substantial number of individual studies, multiple meta-analytic- and systematic-reviews (Ray et al., 2009; Quinn and Fromme, 2011), and calls for integration of subjective response into the National Institute for Mental Health Research Domain Criterion(RDoC) approach to AUD (Ray, 2016). As highlighted by the two prevailing theoretical models born out of this subjective response literature, the most salient conclusion to emerge from this research is that individuals vary in their response to the psychoactive effects of alcohol. 18Within the Low-Level Responders Model (LLRM) individuals with a family history of AU exhibit a lower level of response to the overall psychoactive effects of AU, thus promoting the need for longer, more intense drinking sessions among this population in order to reach desired effects (Schuckit, 2009). Additionally, a parallel line of research, based in the biphasic model of AU, has suggested that both males and heavy drinkers are particularly sensitive to the stimulating and rewarding effects of AU, whereas they are less sensitive to the sedating and aversive effects of AU (Quinn and Fromme, 2011). This pattern of subjective response across stimulating and sedating domains of effects is described by the differentiator model. Within this framework, increased stimulation and decreased sedation associated with AU is suggested to promote problematic drinking by way of increasing the reinforcing-, and attenuating the punishing-, effects of AU respectively (Newlin & Thomson, 1990). These studies help to explain why some individuals, such as males and those with a family history of AU, are particularly susceptible to alcohol use problems. Specifically, that variations across these groups in the psychoactive effects experienced while engaging in AU facilitate problematic drinking practices such as HED. Taken together, this immense body of research highlights how the psychoactive effects of AU are not constant across individuals and, moreover, how these effects play a significant role in the development of AU related problems. In addition to these variations of subjective response to alcohol across individuals, psychoactive effects have been shown to vary within-individuals such that different patterns of psychoactive effects may emerge from one drinking event to the next, even within a single individual. Further, it is likely that these variations of subjective response are related to a number of moderating biopsychosocial factors (Brunt et al., 2014; Seigal et al., 2001). 19Perhaps the most well-researched modulator of AU psychoactive effects is the opioid antagonist naltrexone, which is recommended as the first-line pharmacological option in the treatment of AUD by the American Psychiatric Association (Guzman, 2018). A recent meta-analysis investigated four potential mechanisms of action associated with naltrexone: reduced craving, increased negative affect, decreased stimulating subjective response, and heightened sedating subjective response (Ray et al., 2019). Of these four mechanisms, alterations in subjective response, especially increased levels of sedative effects, following naltrexone administration, emerged as the strongest associations to the medication over placebo. The findings of this meta-analysis fall in-line with predictions made by the differentiator model of subjective response: individuals most at risk for negative alcohol related outcomes are those that experience heightened sensitivity to the stimulating-, and reduced sensitivity to the sedating-, effects of alcohol. As such, naltrexone in-part, reduces risk for alcohol related problems by promoting a lower-risk pattern of subjective response whereby drinking is associated with reduced stimulation and heightened sedation. Over time, repeated co-use of naltrexone and alcohol mitigates the likelihood that an individual will return to negative outcomes such as problematic drinking patterns, and both short- and long-term AU related consequences (Leeman et al., 2008; Morgenstern, 2012; Murphy, 2021). In line with this, naltrexone is typically prescribed for daily use administered over a period exceeding three months (Anton, 2008) as the influence of Naltrexone on the psychoactive effects of AU are exerted acutely. As such, in order for the medication to exert its primary mechanisms of action, its influence on the subjective response to AU, it is necessary that naltrexone be administered with alcohol such that the effects overlap. 20 To summarize, the prevailing view of AU psychoactive effects is that alcohol’s effects are biphasic wherein the earlier portion of a drinking event is characterized by stimulating psychoactive effects, whereas sedating effects are prominent as blood alcohol concentrations begin to drop. Additionally, between- and within- person moderators of AU psychoactive effects have been well-established and associated with alcohol outcomes. These studies suggest that a less stimulating, more sedating, effect profile is the safer response pattern associated with AU, and that promoting such a response pattern, as with the case of naltrexone, may help to attenuate long-term negative alcohol outcomes. Within the context of cannabis alcohol co-use, the literature on the psychoactive effects of AU suggests that if co-use were to promote a safer pattern of AU psychoactive effects, it may mitigate both the short- and long-term harms and consequences related to alcohol. Psychoactive Effects of Cannabis Use The structure of psychoactive effects associated with CU are not generally thought to mirror the biphasic psychoactive effects of AU (Green, 2003). Nonetheless, measurements of BAES sedation and stimulation following CU have been incidentally included in two studies (Ballard & de Wit, 2001; Schacht et al., 2009), both of which failed to find a significant increase in either BAES sedation or stimulation. This null influence of CU on BAES domains may not be generalizable outside of the laboratory given that the cannabis administered in these studies have substantially lower concentrations of TetraHydroCannabinol (THC), the primary psychoactive compound associated with cannabis, relative to the cannabis used by most individuals. Additionally, it is possible that the psychoactive effects of CU are not adequately measured by the BAES, which is designed for use in measuring the psychoactive effects of AU. Indeed, while both sedating and stimulating effects have been noted following CU (Green, 2003; Hart et al., 212001; Hart et al., 2002), the more specific descriptions of these effects differ from those associated with AU. For example, the stimulating effects of CU include cognitive enhancement and expansion, whereas these are rarely mentioned within the AU psychoactive effects literature (Green, 2003; Morean & Corbin, 2010). Moreover, a non-negligible number of CU psychoactive effects, such as hunger and hallucination, cannot be neatly categorized into either stimulating and sedating psychoactive effects. Taken together, the available literature suggests that CU does not have a major influence on subjective states measured by the BAES, which may reflect methodological limitations of past research, or a distinct subset of psychoactive effects that may be stimulating and sedating in ways not captured by the BAES. Beyond these investigations into the BAES domains of subjective states, the psychoactive effects of CU are most commonly measured with the use of Visual Analog Scales (VAS). These VAS measures typically include a number of adjectives used to describe subjective states and ask respondents to rate the extent to which they are feeling a particular way. Although no standard set of VAS adjectives exists to describe CU psychoactive effects, examples of popular choices across studies include: drowsy, happy, creative, and calm (Kleinloog et al, 2014; Zeiger et al., 2010). A principle component analysis of the VAS adjectives typically used in cannabis studies has revealed up to three clusters of effects associated with CU (Kleinloog et al, 2014). First, effects related to perception were the alteration of time, feeling high, and difficulty controlling thoughts. Next, relaxation effects included feeling mentally slow, drowsy, or dreamy. Finally, effects related to dysphoria were feeling as though events, objects, or people had specific meaning, hearing voices that were not there, and feeling paranoid. The findings of this principle component analysis falls in line with current conceptualizations of CU psychoactive effects which tend to highlight relaxing and sedating effects as the hallmarks of the CU subjective states, 22but note a less pronounced presence of stimulating effects such as sociability (Kleinloog et al, 2014). Thus, although increases in stimulated and sedated subjective states are not absent from CU, the effects of CU are likely more complex than the biphasic model proposed to fit AU. Extending these findings from CU and AU psychoactive effects to the potential experience associated with co-use leads to the suggestion that CU may impact the subjective experience of AU intoxication in three ways: exacerbating the sedating states of AU, generating new perceptive effects which would be absent with AU alone, and a possible influence on AU stimulation. Nonetheless, these cross-over effects can only be confirmed through direct investigations of cannabis-alcohol co-use, wherein participants have engaged in CU and AU in a limited-time window such that CU and AU effects overlap. Direct Investigations into the Psychoactive effects of Cannabis-Alcohol Co-Use In contrast to the wealth of literature describing the psychoactive effects of CU and AU on their own, relatively few studies have directly examined the subjective experience associated with cannabis-alcohol co-use. A review conducted by Yurasek and colleagues (2017) notes that the majority of co-use studies have focused on the behavioural impairments associated with cannabis-alcohol co-use. In these studies, co-use has been linked with greater intoxication relative to the administration either substance on its own (Chait & Perry, 1994), and worse simulated driving performance relative to either CU or AU (Chait & Perry, 1994; Ramaekers, 2000; Ramaekers et al., 2011). Additional research specifically investigating the pharmacokinetic interactions of co-use suggests that co-use increases overall cannabinoid blood plasma levels (Lukas & Orzoco, 2001; Hartman et al., 2015; Hartman et al., 2016), decreases wanting for further alcohol (Ballard & de Wit, 2011), and dose dependently impacts psychoactive effects such that co-use was associated with increases in pleasurable effects at low 23doses of alcohol, but decreased positive effects at higher doses (Lukas & Orzoco, 2001). Only one study has directly investigated the impact of co-use on BAES stimulating and sedating AU effects. Study results showed that co-use was associated with increases in subjective intoxication, but not associated with significant changes in either the stimulating or sedating subscales of the BAES (Ballard & de Wit, 2011). While laboratory studies provide strong internal validity, they do not map onto the typical experience of co-use outside of a research setting, thus there is a need for replication of these findings in the contexts that typify co-use. Findings from observational studies align with these laboratory findings insomuch as cannabis-alcohol co-use is reported to increase overall levels of subjective intoxication (Linden-Carmichael et al., 2020; Sokolovsky et al, 2020). Additionally, surveys of young adults confirm that the term cross-faded, used to describe co-use behaviour or the experience of overlapping AU and CU psychoactive effects, is a prevalent term among young adults (Patrick & Lee, 2018). Moreover, having strong cross-faded motives, such as “to be cross-faded” and “to increase the positive effects [of] alcohol”, have been linked to higher rates of CU and AU related consequences (Patrick et al., 2020). Reports gathered from two qualitative reviews suggest the psychoactive effects of co-use predominantly include: reduced sociability at higher levels of AU (Boyle et al., 2021; Price Wolf et al., 2018), increased sociability at lower levels of AU (Boyle et al., 2021), increased feelings of relaxation, reduced desire for further AU, mitigation of adverse AU effects, and facilitation of sleep after a heavy drinking event, (Price Wolf et al., 2018; Boyle et al., 2021). These findings validate suggestions that co-use, broadly, is adequately described as a crossing over of effects associated with CU and AU on their own. Taken together, the findings of laboratory, observational, and qualitative studies tend to characterize the psychoactive effects of co-use as a crossing-over of effects known to AU and 24CU, rather than a completely unique effect distinct from either substance alone (Boyle et al., 2021; Hartman et al., 2015; Hartman et al., 2016; Lukas & Orzoco, 2001; Patrick & Lee, 2018; Price Wolf et a., 2018). Additionally, studies note that co-use is perceived as more intoxicating than AU alone (Linden-Carmichael, 2020; Sokolovsky et al, 2020), and is typically described as a more relaxed experience than AU, but, in some cases may serve to increase stimulating AU effects such as sociability (Boyle et al., 2021; Patrick & Lee, 2018 Price Wolf et a., 2018). The non-uniform influence of CU on AU stimulating effects may be related to a number of factors that can vary across co-use sessions such as level of alcohol and cannabis intoxication (Lukas & Orzeko, 2011), cannabis type used (Brunt et al., 2014), and environmental context (Siegel, 2001). Nonetheless, these direct examinations of cannabis-alcohol co-use generally align with what might be expected from overlapping cannabis and alcohol effects, wherein the predominantly relaxing effects of cannabis serve to exacerbate the sedating elements of alcohol’s subjective experience. The potential for CU to increase sedation and decrease stimulation associated with AU has important implications related to the harm-profile of co-use. First, a large body of research suggests that the medication Naltrexone exerts its therapeutic effect in reducing problematic AU by way of heightening patient’s sensitivity to the sedating effects of AU, while attenuating the stimulating AU effects (Ray, 2019). With respect to AU more broadly, this pattern of subjective response to alcohol has been associated with reduced craving for alcohol (Quinn & Fromme, 2011), and both short- and long-term alcohol related harms, whereas the opposite pattern has been linked with increased craving and number of drinks (Leeman et al., 2008; Morgenstern; 2012; Murphy, 2021). At present, it appears that the psychoactive effects associated with cannabis-alcohol co-use are more sedating than AU alone. With respect to AU stimulation, the 25extent to which co-use is stimulating depends on moderating factors such as number of drinks. Given the robust associations between subjective response of AU and outcomes, whether cannabis attenuates or exacerbates the stimulating and sedating effects of AU has direct implications for our understanding of the harm associated with this practice. Specifically, it may be that psychoactive effects mediate the associations between co-use and number of drinks, but this indirect pathway has not yet been investigated. Regret Previous investigations into cannabis-alcohol co-use have focused primarily on the impact of co-use onto short- and long-term number of drinks (Lee et al., 2020; Lipperman-Kreda, 2017; Mallet et al., 2019; Sokolovsky et al., 2020). The strength of using consequences as an outcome is that harm can be counted objectively, and thus, is less vulnerable to biases that emerge with the use of subjective outcomes. Nonetheless, these studies leave unanswered questions with respect to the co-use experience. Specifically, objective harm does not help to understand the extent to which the individual who engaged in co-use ultimately perceived a drinking event to be harmful. A drinking event with consequences could be perceived by some as deeply harmful or completely benign depending on how an individual weighs these harms and how these harms are balanced with positive outcomes. Elucidating these components of the co-use experience are essential to the formulation of a holistic characterization of the co-use experience, and they can guide understanding about the experience of co-use from the perspective of the individual engaging in the behaviour. Perceived harmfulness is not always congruent with objective harm experienced in each substance use event (Davies & Joshi, 2018). For example, harms do not map on perfectly to feelings of regret following a drinking event, which suggests a mismatch between objective and 26perceived harm in some contexts. Regret is a self-reflective emotion that speaks to an incongruency between desired and realized outcomes of some past decisions made (Zeelenberg et al., 1998). Theoretically, regret can be distinguished from objective harm in three ways. First, regret accounts for the weighting of specific harms. Additionally, regret considers the balance of positive and negative outcomes associated with a decision. Third, feelings of regret are related to all possible experiences within a drinking event. For an individual that embarrasses themselves because of intoxication, the overall decision to use substances may not be regretted if the individual does not feel that social embarrassment is meaningfully harmful, or if this consequence is outweighed by many positive outcomes. Alternatively, it is possible that the harm in question is uncommon and therefore not captured in measures of objective harm. For example, negative interactions with law enforcement are rarely asked in measures of harm, yet these types of interactions occur in higher rates within substance use events (Pollack, & Humphreys, 2020). Thus, regret is a useful outcome to consider in parallel to objective harm because it captures a more personal and complete snapshot of the harm associated with a specific decision. In addition to acting as a subjective proxy to objective measurements of harm, feelings of regret are also qualitatively distinct from harms. Regret predicts distinct positive and negative future outcomes relative to count measurements of number of drinks (Davies et al., 2024). Unlike number of drinks of past drinking events, which do not predict future drinking behaviour (Lee et al., 2018), feelings of regret are adaptive insomuch as they can guide future decision towards better outcomes such as safer or reduced future drinking (Epstude, 2008). In contrast to this potential protective benefit associated with feelings of regret, there are also contexts wherein regret is a risk factor for future negative outcomes. For example, feelings of regret have been linked to reduced life satisfaction, poorer subjective wellbeing (Jokisaari, 2004; Lecci et al., 271994), and increased depressive symptomology (Allaert et al., 2019). Taken together, regret is associated with unique positive and negative outcomes relative to objective harm. Limited research exists on the topic of regret and cannabis-alcohol co-use, although in general, regret appears to be more prevalent and extreme with AU relative to CU. For example, the average person reports regret about their drinking in one third of their drinking occasions (Winstock, 2019), whereas regret over a single occasion of CU appears to be relatively rare (Terry-McElrath et al, 2022). Additionally, qualitative comparisons across CU and AU indicate that CU is less likely to result in behaviours perceived as extremely regrettable, which are often choices related to behavioural under-control such as risky sexual encounters (Palamar et al., 2018). Indeed, CU intoxication is not generally associated with the short-term harms prevalent in AU, and especially HED. Nonetheless, in the absence of direct investigations of regret within co-use events, it is unclear whether CU remains less regretful within the context of a drinking event. Given that CU and AU are occurring simultaneously within an individual, co-use provides a unique opportunity to investigate these differences in regret, with all other factors held constant. As such, research examining regret within co-use events can provide novel insight into co-use regret, and the relationship between CU- and AU-regret. Taken together, feelings of regret within the context of cannabis-alcohol co-use events represents an important area of future research because the constructs of regret and objective harm are theoretically and qualitatively distinct. In addition to being uniquely predictive of both positive and negative outcomes (Epstude, 2008; Jokisaari, 2004; Lecci et al., 1994), regret captures an individual’s overall impression of their decision, including weighting of harms against positive outcomes. As such, a strength of this approach is that it allows for a complete snapshot of the individuals perception of their decision to engage in substance use. 28Intention Fulfilment Co-use events may be associated with a better balance of positive and negative outcomes relative to AU alone. Past research has suggested that weekly CU is associated with greater experiencing of positive and negative alcohol related consequences (Gunn, 2018), suggesting that co-use may promote both harm and benefit. Additionally, young adults are motivated towards co-use for enhanced psychoactive effects and social oriented motivations (Patrick, Fairlie, & Lee, 2017), suggesting the presence of positive outcomes in these areas. While informative, these studies do not speak directly to the individual’s overall impression of co-use. One way to measure the overall experience of a co-use event is by simply asking participants the extent to which a decision achieved a desired outcome. For example, an individual that reports motivations for substance use oriented towards coping, can be asked following the event to reflect on how well their decision to use substances helped them cope. Participant responses would account for weighted positive- and negative-outcomes by way of indicating the extent to which an event successfully fulfilled initial intentions for use. Intention fulfilment measured as such can augment findings related to regret and harm, by providing insight into the domains wherein co-use is associated with differences in the balance of positive- and negative- outcomes. Additionally, by capturing the whole co-use experience, rather than focusing on only negative outcome, intention fulfilment centers the research question to the lived experiences of people who use drugs. This centered perspective can provide context to our understanding of co-use by capturing the reasons for use even with the potential for increased risk for harms. To date, no studies have taken this approach in their investigations into co-use outcomes. Enhanced understanding in this area would be a step towards a more comprehensive 29characterization of cannabis-alcohol co-use, and therefore, is an important future direction for co-use research. Summary Despite harm reduction policies and guidelines that seek to reduce cannabis-alcohol co-use practices, a close examination of the available literature does not clearly support the idea that co-use is more harmful than AU alone. A growing body of literature suggests that number of drinks is the driver of harms associated with co-use (Lee et al., 2020; Lipperman-Kreda, 2017; Mallet et al., 2019; Sokolovsky et al., 2020). Additionally, emerging research has indicated that co-use is protective of harm at higher levels of drinking (Sokolovsky et al., 2020), which suggests that the influence of co-use onto harm is not consistent across all levels of drinking. Moreover, little is known about the mechanisms that underly any protective or harmful effects of co-use, but such information is necessary for a complete characterization of co-use and its relationship to harm. Together, these findings highlight a nuanced relationship between co-use and harm which is complicated by third variable effects such as number of drinks. A greater understanding of these complex dynamics is necessary to produce accurate and informative harm reduction strategy. Past research has highlighted three potential indirect pathways by which co-use may serve to influence harm: number of drinks, AU-sedation, and AU-stimulation. First, co-use may decrease total number of drinks which has been closely linked to negative short- and long-term AU outcomes (Taylor et al., 2010; Piazza-Gardner, 2016). As such, engaging in co-use may promote lighter drinking thereby facilitating a safer substance use event. Second, protective effects of co-use that emerge when controlling for number of drinks suggests alternative pathways by which co-use influences harm (Sokolovsky, 2020). Available literature suggests 30that co-use may heighten the sedating effects of AU. Moreover, increased sedation is linked to reduced harms of AU (Quinn and Fromme, 2011). As such, a second mechanism by which co-use could relate to harm is through AU sedation whereby co-use is linked to reduced harms due to its sedating influence on the psychoactive effects of AU. Finally, a third mechanism that may drive the relationship between co-use and harm is through the stimulating effects of AU. Specifically, the stimulating effects of AU tend to be associated with increased harm (Quinn and Fromme, 2011), thus it is possible that co-use attenuates AU stimulation thereby reducing harm. Taken together, co-use on a night of drinking rather than AU alone may reduce exposure to harm by encouraging reduced number of drinks, and additionally, co-use may facilitate the onset of a psychoactive effect profile associated with reduced harms. Previous studies have added substantially to our understanding of the interrelationship between CU, AU, HED, and harm. However, a number of gaps remain to clarify if, and how, co-use may impact the harm associated with AU. First, potential co-use related substitution effects may have been obscured by an operationalization of co-use that did not distinguish CU-post-AU events from drinking sessions where cannabis was used before or during AU. Further research conducted with this distinction considered, may help to clarify the potentially substitutive impact CU has on AU. Additionally, in the case of a genuine substitution effect associated with cannabis-alcohol co-use, a pathway from co-use to reduced harms by way of reduced number of drinks follows. Nonetheless, likely due to obscured substitution effects in previous studies, such a mediation model has never been explicitly tested and represents a prominent gap in the extant cannabis-alcohol co-use literature. Additional literature gaps relate to the impact that co-use may exert on the psychoactive effects associated with AU alone. Specifically, limited studies have directly examined the 31psychoactive effects of co-use within the context of ecologically valid drinking conditions. As such, whether CU increases or decreases the sedating and stimulating elements of AU effects is left unknown. The sedating elements of AU are typically experienced as unpleasant, such as nausea and grogginess, whereas the stimulating psychoactive effects are often considered to be pleasant and rewarding, such as feeling buzzed. Thus, alterations to these psychoactive effects may drive individuals towards safer behaviour such as going to sleep in place of extending a drinking session. In line with this, should co-use modulate an individual’s subjective experience of drinking, the well-established links between subjective response to AU and AU-related harms (Quinn and Fromme, 2011) suggest a pathway wherein co-use may be associated with reduced harm by way of creating a psychoactive experience of drinking that is less dangerous. This potential mediation model would apply to both the stimulating and sedating domains of AU psychoactive effects, but neither of these models have been directly tested, and therefore AU sedation and stimulation represent second and third gaps in the literature. Finally, a fourth gap exists in the disparate literature relating to co-use short-term outcomes beyond the narrow focus of objective harm. Examining feelings of regret following co-use, and domain-specific intention fulfilment of co-use relative to AU can further our understanding of co-use outcomes by providing a subjective complement to the line of research investigating the relationship between co-use and harm. Regret and intention fulfilment, which account for the balance of subjectively weighted negative and positive outcomes, may be better suited to capture the overall perception of harm and benefit in each co-use event relative to objective measures of harm. Nonetheless, these areas have not yet been subject to empirical investigation, and thus, represent additional gaps in the literature seeking to characterize the short-term outcomes of co-use. 32Understanding cannabis-alcohol co-use is important for the establishment of effective harm-reduction guidelines and policies. With respect to harm-reduction guidelines, understanding if, and how, co-use can either increase or decrease harm can help inform those who co-use of safe practices. Likewise, some of the policies from private and public organizations that directly seek to prohibit or mitigate cannabis-alcohol co-use can be adjusted to reflect the potentially harm reducing qualities of the practice. For example, the advertisement of cannabis products at liquor licensed establishments is currently restricted within British Columbia (British Columbia Liquor and Cannabis Regulation Branch, 2019). Should the findings of the present study suggest a harm-reducing effect associated with cannabis-alcohol co-use, such prohibitions could be removed or adjusted. Taken together, a deeper understanding as to if and how cannabis acts as a substitute for alcohol can have far reaching public health and policy implications. To summarize, examining three, largely unexplored, potential mechanisms by which cannabis-alcohol co-use may impact harms could help enrich our understanding of the practice. First, co-use may reduce harms by attenuating the intensity of an AU event with respect to number of drinks (Figure 1.). Second, co-use may reduce harms by way of altering sedation associated with AU intoxication (Figure 2.). Finally, co-use may impact the stimulating effects of AU in a way that alters the likelihood of harm associated with co-use (Figure 3). These mechanisms are important in delineating if, and how, CU relates to AU harms. Beyond exploration of these mechanistic effects, investigation into the relationship between co-use and alternative outcomes, such as feelings of regret and perceived intention fulfilment of the event, are important next steps in the formulation of a comprehensive characterisation of cannabis-alcohol co-use. 33Figure 1. The Proposed Relationship Between Co-Use and Harms via Number of drinks Figure 2. Proposed Relationship Between Co-Use and Harms via Alcohol Use Sedation. Co-use Number of drinks Harms Co-use Sedation Alcohol related consequences Harms 34Figure 3. Proposed Relationship Between Co-Use and Harms via Alcohol Use Stimulation Purpose of the Present Study The present study directly builds on Rootman (2020) in several ways. Specifically, the findings of this earlier study suggested that, when substance use ordering is considered such that CU-post-AU events are distinguished from other co-use events, cannabis-alcohol co-use is associated with reduced number of drinks relative to AU alone. As such, the present study aimed to both replicate these findings and build on them by integrating direct assessments of negative co-use harms and psychoactive effects in order to further clarify the interrelationships between cannabis, alcohol, and harms. This study was the first to directly explore the roles that number of drinks, AU sedation, and AU stimulation have in number of drinks. Additionally, these mechanisms were investigated with the use of an EMA design and operationalization of co-use that accounts for the order which substances are used. Specifically, co-use in the present study was defined as the use of CU before/during a drinking session, whereas AU events included only AU alone events. CU-post-Co-use Stimulation Alcohol related consequences Harms 35AU events were not included in analysis as it was unclear whether harms occurred prior to or following co-use. Additionally, the present study pursued novel exploration of co-use outcomes such as regret and intention fulfilment. Through this assessment of cannabis-alcohol co-use, this research aimed to distinguish the ways in which co-use relates to positive and negative outcomes, and moreover, highlighted the circumstances most likely to promote these effects. This study expanded on the understanding of co-use through examination of the following primary and secondary aims: Aim 1: Examine the relationship between co-use and harm without number of drinks considered. H1: Relative to AU alone, engaging in co-use will be associated with greater harms. Aim 2: Examine the moderating influence of number of drinks onto the relationship between co-use and harm. H2: Number of drinks will significantly moderate the relationship between co-use and harm. Specifically, as number of drinks increases, co-use will be associated with increasingly greater protective effects to harm relative to AU alone. Aim 3: Examine how co-use relates to the number of drinks consumed across the contexts of light drinking events and HED events. H3: Within the context of HED, co-use events will be associated with fewer drinks. H4: Within the context of light drinking, co-use events will not be associated with number of drinks. 36Aim 4: Examine mediation of the relationship between co-use and harms via number of drinks. H5: Within the context of a HED, number of drinks will mediate the relationship between co-use and harms such that co-use will be associated with fewer harms relative to AU alone via an association to lower number of drinks (Figure 1). H6: Within the context of a light drinking, number of drinks will not mediate the relationship between co-use and harms (Figure 1). Aim 5: Examine mediation of the relationship between co-use and harms via AU sedation. H7: AU related sedation will mediate the relationship between co-use and harms such that co-use will be associated with fewer harms through greater levels of AU sedation (Figure 2). Aim 6: Examine mediation of the relationship between co-use and harms via AU stimulation. H8: AU related stimulation will mediate the relationship between co-use and harms such that co-use will be associated with fewer harms through lower levels of AU stimulation (Figure 3). Aim 7: Examine how co-use relates to AU regret and CU regret. H9: Co-use will be associated with lower AU regret. H10: Within co-use events, AU regret will be higher than CU regret. Secondary Aim: Examine how co-use relates to intention fulfilment. H11: Among participants that reported motivations to engage in a night of substance use for the purposes of enhancement, co-use will be associated with enhancement relative to AU. 37H12: Among participants that reported motivations to engage in a night of substance use for the purposes of coping, co-use would be associated with coping relative to AU. H13: Among participants that reported motivations to engage in a night of substance use for the purposes of socializing, co-use would be associated with socializing relative to AU. H14: Among participants that reported motivations to engage in a night of substance use for the purposes of conformity, co-use would be associated with conformity relative to AU. H15: Among participants that reported motivations to engage in a night of substance use for the purposes of expansion, co-use would be associated with expansion relative to AU. 38CHAPTER 2: Methods Procedure Participants were young adults who were recruited from undergraduate psychology courses. In line with recommendations by Arend and Schäfer (2019) for achieving the desired power to detect small to medium effects, the study had sought to recruit a sample of 60 participants for multiple assessments. Participants who were not between the ages of 18-25, who could not read or understand English, or who did not report engaging in HED at least once per month were excluded from the study. The study consisted of two parts. First, eligible participants had completed an online informed consent process in addition to a baseline survey that took approximately 30 minutes to complete. Participants had the option to be compensated for this portion of the study with course credit or an equivalent amount of financial remuneration ($10 CAD). Following the completion of this online survey, participants were automatically enrolled in the EMA portion of the study. Daily EMAs were administered to participants, at a minimum, once daily at 7:00pm. Participants were first asked to indicate whether they had already begun or planned to begin a drinking event before going to sleep. Participants who reported no plans to drink on the assessment day were thanked for their participation and were provided no more questions. Affirmative responses to the drinking plan question branched more questions to participants within this daily EMA, and additionally, triggered two more EMAs. Specifically, participants were asked within the daily EMA when they expected to have their last drink of the evening which would be used to identify optimal times to trigger a follow-up EMA. Participants were also asked to report on their substance use motivations for the upcoming drinking event. Additionally, an end-of-evening EMA assessment was provided to participants one hour before 39their expected last drink of the evening, and participants were asked to complete this assessment as close as possible to their final number of drinks. This end-of-evening EMA assessed psychoactive effects associated with AU or co-use. If the participant reported no drink consumed at the end-of-evening EMA but still planned to, the end-of-evening EMA was retriggered every 30 minutes until the participant either initiated drinking or reported that they no longer planned to. Finally, a morning-after EMA was provided to participants at 2:00pm following a night of drinking to primarily assess AU and co-use related number of drinks. Participants were compensated as follows: each daily EMA corresponded to a 20-cent payment to participants, whereas end-of-evening and morning-after EMAs corresponded to a $1 payment. Participants received a bonus $2 for completing all three surveys in a row. Participants were enrolled in the study for a maximum of whichever of the following occurred first: 100 study days, completion of 20 end-of-evening EMAs, or completion of 20 morning-after EMAs. In cases where participants completed 20 end-of-evening or 20 morning-after EMAs, they were compensated for the assessments completed plus an additional 20 cents for every day remaining in the 100-day study period. As such, participants who responded to all possible surveys were eligible to receive a minimum of $88 and a maximum of $108 for their participation in the EMA portion of the study, in addition to the $10 associated with the baseline questionnaire. The limit of 20 observations was implemented to maintain a balance of between- and within-person observations within the dataset. The baseline questionnaire had been administered to participants prior to the initiation of EMA surveys and included questions intended to query demographic factors such as gender, age, ethnicity, health, and others. Additionally, substance use patterns were assessed in the baseline survey with a specific focus on CU, AU, and substance use history. Particularly relevant to the 40present study were questions used to derive between-person average rates of CU and AU. Participants were asked to estimate the number of drinking days and CU days in a typical month of substance use. Appendix A included all 14 questions included in the baseline questionnaire. EMA surveys were sent to participants' mobile phones via the Metricwire mobile phone application, which is available for free online on both the Google Play and Apple App stores. The daily EMAs in the study included up to two questions depending on branching logic, whereas the end-of-evening EMA included up to 13 questions, and the morning-after EMA included up to 38 questions. However, in all of these assessments, participants were shown select questions due to branching logic. Daily EMAs primarily acted as a trigger for the end-of-evening and morning-after EMAs, which were intended to measure psychoactive effects and post-event harms respectively. Appendix B includes all questions associated with each of the three EMA surveys in the order in which they appeared. Measures Harms The primary purpose of the morning-after EMA was to assess harms associated with the previous night’s drinking/co-use event. Mallet and colleagues (2019) derived a co-use related consequences measure by synthesizing items taken from validated questionnaires on AU and CU consequences into one, broader, co-use consequence measure. Specifically, these authors drew from the Young Adult Alcohol Problems Screening Test (YAAPS; Hurlburt & Sher, 1992), the Young Adult Alcohol Consequences Questionnaire (YAACQ; Read et al., 2006), and the Marijuana Consequences Questionnaire (Simons et al., 2012) to create a 33-item checklist of consequences pertaining to the previous drinking event. Participants were provided the complete list of consequences and asked to report if they had experienced the consequence throughout the drinking event being assessed. 41Co-Use The within person co-use variable was defined as CU either before or during an AU event. Given the findings suggesting that the order in which individuals engage in CU relative to AU is an important consideration in the operationalization of co-use, a question intended to tap into substance use ordering was branched to all participants who reported using both AU and CU within two hours of each other. Specifically, participants were shown a hypothetical timeline which delineated AU by first and last drink, thereby creating three discrete sections: before the first drink, between the first and last drink, and after the last drink. Participants were asked to indicate when they used cannabis out of these three options. Multiple selection was permitted for participants who engaged in CU more than once. When CU only occurs after AU it becomes unclear which outcomes, such as a specific consequence, occurred within the period when AU was consumed without CU, or when the two substances were co-used. As such, observations wherein co-use occurred only after AU were not included in analyses in the present study. Thus, the within-person co-use variable was binary and included observations wherein AU was used without CU, and observations wherein CU occurred before or during AU. Tendency to Co-Use Responses to the within-person co-use variable were aggregated within individual to derive the mean proportion that a given participant used AU alone or co-used cannabis and alcohol. Number of drinks The daily measure of number of drinks was measured in any of the three EMA assessments whenever a participant reported drinking prior to the assessment. The question: “how many drinks did you have today” was used to assess the number of drinks consumed within 42the daily and end-of-evening EMAs, whereas “how many drinks did you have yesterday” was used in the morning-after EMA. Along with the question, participants were shown an image depicting standard drink amounts across beer, wine, and hard-liquor alcoholic drinks. In order to delineate distinct drinking events within one day, such as when a participant had one drink at 11:00 am and then four drinks later at 9:00pm, these number of drinks items were followed with the question: “were all these drinks consumed as one drinking event” In other words, no more than three hours separating two drinks”. Participants who answered no, thereby reporting an interrupted drinking day, were displayed the following question: “How many drinks were consumed in the most recent of these drinking events.” Finally, participants who reported drinking were asked to respond to the following: “Did you use cannabis within two hours of this drinking event?” Typical Number of drinks The between-person variable of number of drinks captures the typical number of drinks that individuals would have in a substance use event. This measure was derived from item-2 of the self-report version of the Alcohol Use Disorders Identification Test (Allen et al., 1997). The AUDIT is a 20-item clinical measure of problematic AU behaviour that is empirical validated and widely used (Reinert & Allen, 2002). Item-2 asked participants “How many drinks containing alcohol do you have on a typical day when you are drinking?” with response options including: (1 or 2), (3 to 4), (5 to 6), (7 to 9), and (10 or more). Responses indicated how much a participant tends to drink within a given drinking occasion. Sedation and Stimulation Psychoactive effects of AU and co-use were assessed with the Brief-Biphasic Alcohol Effect Scale (B-BAES; Rueger & King, 2013), a 6-item abbreviated version of the full BAES. 43Three items of the B-BAES assess the stimulating effects of AU and three items assess the sedating effects of AU. This abbreviated version has shown good concurrent reliability with the full version (Rueger & King, 2013), and, similar to the longer version, had been linked to a number of important AU-related outcomes such as short- and long-term consequences (Rueger & King, 2013; King et al., 2014). The B-BAES was included in Appendix C. Typical sedation and stimulation The between-person variable of sedation and stimulation was calculating by mean aggregating all within-person observations to the between person level. As such, typical sedation represents the participant’s mean level of sedation across all observations, whereas typical stimulation represents participant’s mean level of stimulation across all observations Regret About Cannabis and Alcohol Use CU and AU regret was measured in the morning-after survey with two questions pertaining to each substance. Specifically, the day following a drinking or co-use event all participants were asked to rate, on a 5-point Likert scale ranging from strongly disagree to strongly agree, the extent to which they agree with the following statements: “It was the right decision to drink alcohol”, and “I regret my decision to drink alcohol.” Additionally, participants that reported co-use were asked to report on the extent to which they agreed with these statements: “It was the right decision to drink alcohol,” which was reverse coded, and “I regret my decision to drink alcohol.” The mean of the two variables was calculated and used as a combined measure of regret. Typical Motivation Between-person motivations for AU were assessed once at baseline using the Drink Motives Questionnaire-Revised (DMQ-R). The DMQ-R is a 20-item validated scale of trait-level 44motivations for AU scored on a 5-point Likert scale ranging from (almost never/never) to (almost always/always). For example, participants were asked to report how frequently they drink “to be sociable” or “to forget your worries.” From these items, the following four motivation domains were derived: social, enhancement, coping, conformity. Daily Motivation Within-person motivations for substance use were assessed within the daily survey among participants that reported that they already have-, or planned to-, drink alcohol in the night. Participants were asked “Right now, how motivated are you to use substances (e.g., alcohol or cannabis) for the following reasons:” and then were provided prompts corresponding to the motivational domains of social, enhancement, coping, conformity, and expansion. For example, the domain of conformity was measured by asking participants, “Because my use of these substance(s) will help me avoid feeling left out”. Responses were provided on a five-point Likert scale ranging from (1 = Not at all - 5 = Definitely). The full scale can be found in Appendix D. Intention Fulfilment Intention fulfilment was measured by asking a parallel set of daily motivation questions adapted for retrospection. For example, conformity was measured by asking participants “In this drinking/co-use event…My use of these substance(s) helped me avoid feeling left out.” Among participants that had reported these motivations the day before, these follow-up questions indicated the extent to which a specific drinking event fulfilled the desired intentions of a given drinking event. 45Analytical Plan Preliminary Analyses Various elements of AU and co-use events were described in a preliminary set of analyses. Observations were categorized such that four discrete subgroups of drinking events were derived. Drawing from the Canadian low-risk drinking guidelines’ definition of HED, light-drinking and HED subgroups were delineated such that HED was considered four, and five, or more drinks for woman and men respectively. These two groups were further subcategorized to distinguish observations that involved drinking (AU only) from co-use (CU before or during). Thus, the four descriptive categories were: AU-only light drinking, co-use light drinking, AU-only heavy drinking, and co-use heavy drinking. Given that participants varied substantially in the number of responses provided, descriptive statistics were calculated by first averaging responses within-person such that each participant only contributed one observation that represented their mean in each variable. The mean of these within-person averages was then calculated. As such, descriptive statistics represented the average person’s response in each variable, rather than the raw mean response across all observations that would have been biased towards the participants that contributed many observations. The following statistics will be described: 1) Within light drinking events, the mean proportion of responses that were either AU-only HED or co-use HED (summing to 100%). 2) Within heavy drinking events, the mean proportion of responses that were either AU-only HED or co-use HED (summing to 100%). 3) The mean and standard deviation of harms experienced across the four categories. 464) The mean and standard deviation of number of drinks consumed across the four categories. 5) The mean and standard deviation of stimulation experienced across the four categories. 6) The mean and standard deviation of sedation experienced across the four categories. 7) The mean and standard deviation of AU-regret experienced across the four categories. 8) The mean and standard deviation of CU-regret experienced across the two co-use categories. Multi-Level Modeling Due to the non-independent, nested nature of data observations within individuals, multi-level modeling (MLM) was used for all analyses. The statistical packages “lme4”, “MASS”, and “mlma” within RStudio version 2023.12.0+369 were used to construct linear-, generalized linear-, and multilevel mediation- models respectively. Model building strategy for all models are described in Appendix E. To prevent overfitting, models were built in a step wise manner such that variables were only retained if they were found to be significant at the <.1 level and improved model fit assessed through the Akaike Information Criteria (Akaike, 1973). An alpha of .05 was used to determine statistical significance in all analysis. Co-use, being the primary variable of interest, was retained in all relevant models regardless of significance. Likewise, co-use and mediators (number of drinks, sedation, and stimulation) were retained throughout model building at both the between- and within- person level (Yu, Li, 2021) Data Preparation First, the data was inspected for random or erroneous responses to be removed from analyses. Next outliers on the count variables of interest, number of drinks, and harm, were 47identified and windsorized to either the 95th or 99th percentile (Aguinis et al., 2013). The percentiles were selected to mitigate influence of outliers while allowing for a meaningful range of values in the variable of interest. Missing data/imputation Analyses were limited to participants that had complete data on the co-use and harm variables. Sedation and stimulation were included in a different survey than the co-use variable, and therefore, had a high rate of missing data. As such, multiple imputation through the statistical package “mice” was used to replace missing data. Predictor variables in the imputation process included all model predictors and several other variables selected based on strength of bivariate correlation to either sedation or stimulation. A total of ten imputations were computed and results of analyses were pooled following Rubin’s Rules (Marshall, et al., 2009). Primary analysis Aim 1. A generalized multilevel model with a Poisson distribution was constructed to assess H1. Specifically, Hypothesis H1 predicted that, relative to AU alone, engaging in co-use will be associated with greater harms. Aim 2. A generalized multilevel model with a Poisson distribution was constructed to assess H2: the moderating effect of number of drinks in the relationship between co-use and harms. Specifically, Hypothesis H2 predicted as number of drinks increases, co-use will be associated with increasingly greater protective effects to harms relative to AU alone. The full model was built prior to inspection of the co-use X number of drinks interaction which, if found significant, allowed for confirmation of Hypothesis H2. Significant co-use X number of drinks interaction was probed through simple slope analysis (Park & Yi, 2023). The following levels of 48number of drinks were investigated to capture effects at the minimum, maximum and a range within those extremes: 1 drink, 5 drinks, 10 drinks, and 15 drinks. Aim 3. Two sets of generalized multilevel models with a Poisson distribution were constructed to assess the association between co-use and number of drinks through the testing of H3 and H4. Hypothesis 3 predicted that, within the context of HED events, co-use would be associated with fewer number of drinks. Hypothesis 4 predicted that within the context of light drinking events, co-use would not be associated with number of drinks. The full models with number of drinks as the outcome were constructed and hypotheses were tested through inspection of the fixed effect of co-use onto number of drinks Aim 4. Generalized multilevel mediation analyses were conducted to examine mediation of the relationship between co-use and harms via number of drinks. To test H5, which predicted that number of drinks would mediate the relationship between co-use and harms within a HED context, the indirect effect of number of drinks was inspected for significance and direction. Similarly, the indirect effect was inspected in the testing of H6 which predicted that co-use would not be mediated by number of drinks in its relationship to harms within the context of light drinking events. Aim 5: Generalized multilevel mediation analysis was conducted to examine mediation of the relationship between co-use and harms via AU sedation. Specifically, H7 predicted that AU sedation will mediate the relationship between co-use and harms such that co-use will be associated with fewer harms through greater levels of AU sedation (Figure 2). This hypothesis was tested through inspection of the indirect effect. If found to be significant in prior analyses, number of drinks was included in the mediation analysis along with sedation. 49Aim 6: Generalized multilevel mediation analysis was conducted to examine mediation of the relationship between co-use and harms via stimulation. This was done through the testing of H8 which predicted that AU related stimulation will mediate the relationship between co-use and harms such that co-use will be associated with fewer harms through lower levels of stimulation (Figure 3). The same process described for Aim 5 was used here. Aim 7: Linear multilevel models were constructed to examine the relationship between co-use and regret. The full model with AU-regret as the outcome was constructed and H9 was tested through inspection of the fixed effect of co-use onto AU-regret. Next, H11 predicted that CU-regret would be lower than AU-regret. This analysis could only be conducted within the subgroup of observed co-use events wherein CU occurred. A difference score was calculated by subtracting the AU regret score with the CU regret score and included as the outcome in the model. H10 was tested by inspection of the model intercept. If significant, the intercept indicated that the difference between AU and CU regret was significantly different from 0. Secondary Aim: Linear multilevel models were constructed to examine the relationship between co-use and intention fulfilment across the following domains: enhancement, coping, socializing, expansion, and conformity. Each analysis was limited only to drinking events wherein the participant reported, on the day of the drinking event, that they agreed or strongly agreed with the idea that their upcoming substance use was motivated by a particular intention. For example, the analysis of intention fulfilment in the domain of enhancement, only included drinking events that were motivated by intentions of enhancement. In other words, if an individual did not report being motivated to drink for a specific purpose, their observation were removed. Thus, five models were constructed with varying subgroups of observations. This 50allowed for analysis of the extent to which an event achieved an outcome, such as being more social, among individuals that had hoped for that specific outcome. Full models with each respective domain of motivation were used as the outcome and H11-15 were tested through inspection of the fixed effect of co-use onto the next day reports of intention fulfilment in each domain. 51CHAPTER 3: Results Participants Across all survey responses, 4162 observations were collected. A total of 52 participants had complete data on, at least, one drinking event and were therefore able to be included in primary analyses. The study duration was 100 days (mean study days: 42, SD = 10.28), and mean observations provided for primary analysis was 6.78 (4.64; Figure 4.). With respect to demographics, 37 (71.2%) of participants identified as female, 12 (23.1%) as male, 1 (1.9%) as Transgender and 2 (3.8%) as non-binary. The mean age in our sample was 20.2 years old (SD = 2.1). 35 (67.3%) participants identified as straight/heterosexual, 3 (5.8%) as Gay /Lesbian/Homosexual, 10 (19.2%) as bisexual, 1 (1.9%) as asexual, and 3 (5.8%) as other. With respect to ethnicity, 33 (63.5%) identified as Caucasian, 2 (3.8%) as Black/African, 1 (1.9%) as Metis, 7 (13.5%) as Asian, 2 (3.8%) as Hispanic, 1 (1.9%) as Middle eastern, 1 (1.9%) as other and 5 (9.5%) as Biracial. With respect to substance use, 45 (86.5%) participants reported at least one drinking event and 34 (65.3%) reported at least one co-use event. Descriptive analysis of co-use patterns across light drinking and HED events can be found in Table 1. Figure 4. Number of Drinking or Co-Use Observations Provided Across Participants by Gender. 52Table 1 Descriptive of Drinking and Co-use Behaviour Light Drinking Event Event type Alcohol Alone Co-use Total % Responses 72.03% 27.97% 100% Harms 1.46 (2.13) 2.68 (3.03) 1.8 (2.38) Number of drinks 1.65 (0.57) 2.38 (0.75) 1.85 (0.62) Sedation 2.6 (2.03) 3.82 (1.13) 2.94 (1.78) Stimulation 4.17 (2.02) 4.87 (1.93) 4.37 (1.99) Heavy Episodic Drinking Event type Alcohol Alone Co-use Total % Responses 54.42% 45.58% 100% Harms 4.21 (3.25) 6.14 (5.04) 5.09 (4.07) Number of drinks 6.65 (2.21) 7.94 (3.78) 7.24 (2.93) Sedation 3.91 (2.31) 4.98 (2.24) 4.40 (2.28) Stimulation 4.91 (3.04) 5.38 (1.89) 5.12 (2.52) Multilevel Modeling For the sake of concision, only relevant effects and final models will be described. Prior to analysis, percentiles were calculated for the number of drinks and harms variable. The harms variable was winsorized to the 5th and 95th percentile which were 0 and 10 respectively, whereas the number of drinks was winsorized at the 1st and 99th percentile which were 1 and 15 drinks consumed. These levels of winsorization were selected to allow for a meaningful range, while mitigating undue influence of outliers. Aim 1: Relationship between co-use and harm without controlling for number of drinks. Hypothesis 1: First, a model was built including only co-use predicting onto harm. In this model, wherein no other variables were controlled, results suggested that co-use was positively associated with harms (β = 0.57, SE = 0.1, z = 5.7, p < .001). In the final model, co-use 53was no longer significant (β = 0.31, SE = 0.2, z = 1.53, p =.12), whereas number of drinks (β = 0.24, SE = 0.03 z = 7.96, p < .001), the between-person measure of number of drinks (β = -.32, SE = 0.11, z = -2.88, p < .01), and the between-person measure of co-use were found to be significantly related to harm (β = .75, SE = 0.37, z = 2.025, p = .042). Aim 2: Moderation of co-use and harm by number of drinks. Hypothesis 2: The interaction between co-use and number of drinks was found to be significant while controlling for these other factors, thus indicating that the impact of co-use is not stable across all levels of number of drinks (β = -.07, SE = 0.03, z = -2.32, p = .02, Figure 5.) Figure 5. Interaction between Co-use and Number of drinks onto Harms This initial analysis was followed-up with probing of the interaction through simple slope analysis at the following levels: 1 drink, 5 drinks, 10 drinks, and 15 drinks. Results 54indicated that the predicted slope of co-use, which refers to the difference between AU only and Co-use, was significant at the level of 10 drinks (β = 0.38, SE = 0.18, z = 2.14, p = .032), and 15 drinks (β = 0.74, SE = 0.31, z = -2.37, p = .018; Figure 6) wherein co-use was protective of harm. The slope of co-use was not significantly related to harm at 1 (β = -0.24, SE = 0.18, z = -1.345, p = .18) or 5 drinks (β = 0.04, SE = 0.12, z = -0.32, p = .75). Figure 6. Interaction between Co-use and Number of drinks onto Harms 55Aim 3: Co-use predicting number of drinks. Hypothesis 3: The final model testing the relationship between co-use and number of drinks within the context of HED indicated that co-use is significantly related to greater number of drinks (β =0.29, SE = 0.11, z = 2.624, p < .01; Figure 7, Table 2) Hypothesis 4: The final model testing the association between co-use and number of drinks within the context of light drinking suggested that co-use is significantly related to increased number of drinks (β =0.23, SE = 0.10, z = 1.771, p=.04). Figure 7. Relationship Between Co-use and Number of drinks Across Light and Heavy Drinking Events 56 Table 2. Co-use Predicting Number of drinks Light-Drinking (n = 200) Heavy Episodic Drinking (n = 106) β SE β SE (Intercept) 0.45** 0.09 1.73** 0.08 Co-use 0.24* 0.12 0.29** 0.11 Tendency to co-use -0.37* 0.15 Typical number of drinks 0.1 0.06 0.09** 0.03 Female 0.34** 0.09 Non-binary/transgender 0.26 0.13 * p < 0.05, ** p < 0.01, ** p < 0.001 Aim 4: Mediation of the relationship between co-use and harms via number of drinks. Three models were constructed for each of the mediation analyses, therefore totalling to six models: three for the light-drinking mediation analysis and three for the heavy drinking analysis. First, the model with co-use and covariates predicting the outcome harm was constructed. Second, the model with co-use and covariates predicting the mediator, in this case number of drinks was built. The third model constructed included the tendency to co-use and covariates predicting the typical number of drinks. The results of these six models and bootstrap mediation analysis can be found in Tables 3-4. Hypothesis 5: Within the context of HED, the effect of co-use onto harm was not significant after controlling for the tendency to co-use and typical number of drinks (total effect β [SD] = -0.27 [0.22], direct effect β [SD]= -0.05 [0.17]). In line with H5, the relationship between co-use and harms was mediated by number of drinks (number of drinks: indirect effect β [SD] =- 570.22 [0.09], upper bound C.I. = 0.44, indirect effect lower bound C.I. = 0.088). Specifically, co-use was found to be associated with increased number of drinks which therein indirectly influenced co-use to be more harmful. Hypothesis 6: In line with predictions of H6, the relationship between co-use and harms was not mediated by number of drinks within the context of light drinking events (co-use: total effect β [SD] = 0.09 [0.21], direct effect β [SD] = 0.16 [0.03], number of drinks: indirect effect β [SD] = -0.06 [0.01], upper bound C.I. = 0.07, indirect effect lower bound C.I. = -0.04). 58Table 3. Models for Number of drinks Mediating the Relationship Between Co-use and Harms Light-Drinking model HED model Outcome [distribution] Variable β F-value β F-value Harms [Poisson] (Intercept) -1.1* 4.77 0.35 1.37 Tendency to co-use 1.4* 6.52 0.49 2.22 Co-use 0.23 1.07 -0.05 0.10 Typical number of drinks -0.17 0.72 -0.22* 4.35 Number of drinks 0.36* 4.28 0.11** 20.99 Daily conform motivations 0.13** 9.35 Number of drinks [Poisson] (Intercept) 0.49** 26.31 1.73** 470.97 Tendency to co-use -0.30 1.61 -0.36* 5.83 Co-use 0.39* 5.59 0.29** 6.89 Typical number of drinks 0.09 2.28 0.10** 7.65 Female 0.34** 3.73 Non-binary/transgender 0.26+ 13.46 Typical number of drinks [Linear] (Intercept) 1.26** 44.15 1.52** 32.03 Typical number of drinks -0.61 1.73 -0.20 0.21 Note. * p < 0.05, ** p < 0.01, ** p < 0.001 59Table 4. Number of drinks as a Mediator in the Relationship Between Co-use and Harms across light drinking and heavy episodic drinking events Light-Drinking Events HED Events 95% CI 95% CI Estimate (SD) LL UL Estimate (SD) LL UL Co-use Total Effect: co-use 0.1 (0.27) -0.42 0.7 -0.27 (0.22) -0.28 0.65* Direct Effect: co-use 0.16 (0.27) -0.45 0.7 -0.05 (0.18) 0.45 0.27* Indirect Effect: number of drinks -0.06 (0.025) -0.04 0.07 -0.22 (0.09) 0.09 0.44* Tendency to co-use Total Effect: co-use 1.35 (0.38) 0.67 2.07* 1.02 (0.30) 0.62 1.75* Direct Effect: co-use 1.48 (0.36) 0.85 2.17* 0.49 (0.24) 0.11 1.06* Indirect Effect: number of drinks 0.09 (0.13) -0.01 0.33 0.55 (0.23) 0.14 1.01* Indirect Effect: typical number of drinks -0.22 (0.08) -0.09 -0.42 -0.023 (0.01) -0.03 -0.01* Note. HED = Heavy Episodic drinking. * = 95% CI does not overlap with 0. Aim 5: Mediation of the relationship between co-use and harms via AU-sedation. Here, the three models included in the previous model were used in analyses and two additional models were added for the typical-sedation and sedation. Thus, 5 models were constructed for the sedation mediation analyses (Appendix F). Hypothesis 7: Contrary to predictions, sedation was not found to significantly mediate the relationship between co-use and harm while controlling for number of drinks (Table 5; indirect effect β [SD] = 0.01 [0.03], upper bound C.I. = 0.03, indirect effect lower bound C.I. = -0.088). Aim 6: Mediation of the relationship between co-use and harms via AU-stimulation. Again, the three models included in the number of drinks models were used in analyses and two additional models were added for typical stimulation and stimulation. Thus, 5 models were constructed for the stimulation mediation analyses (Appendix F). 60Hypothesis 8: Stimulation did not mediate the relationship between co-use and harm while controlling for number of drinks (Table 6; indirect effect β [SD] = -0.03 [0.027], upper bound C.I. = 0.07, indirect effect lower bound C.I. = 0.03). This finding does not align with the prediction made by H8 which expected that stimulation would mediate the relationship between co-use and harm. Table 5. Sedation as a Mediator in the Relationship Between Co-Use and Harms 95% CI Estimate (SD) LL UL Co-use Total Effect: co-use -0.30 (0.20) -0.49 0.24 Direct Effect: co-use 0.035 (0.15) -0.28 0.3 Indirect Effect: number of drinks -0.35 (0.08) -0.29 -0.01* Indirect Effect: sedation -0.01 (0.03) -0.09 0.03 Tendency to co-use Total Effect: co-use 1.06 (0.26) -0.49 1.62 Direct Effect: co-use 0.8 (0.24) -0.28 1.4 Indirect Effect: number of drinks 0.16 (0.13) -0.04 0.43 Indirect Effect: typical number of drinks 0.04 (0.05) 0.04 0.05* Indirect Effect: sedation -0.003 (0.03) -0.08 0.06 Indirect Effect: typical sedation -0.06 (0.03) -0.002 0.13 Note. * = 95% CI does not overlap with 0. 61 Aim 7: Co-Use Predicting Regret Two linear multilevel models were built to investigate the relationship between co-use and regret. Hypothesis 9: In line with the prediction of H9, co-use events were significantly related to lower levels of regret relative to when AU was used alone (β =0.41, SE = 0.19, t = -2.5, p =.01). The final model included the fixed effects of number of drinks (β = 0.02, SE = 0.02, t = .83, p =.41), and motivations to use substances for the purposes of enhancement (β = 0.19, SE = 0.05, t = 2.97, p <.001). No random effects improved model fit, and thus, were included in the model. Hypothesis 10: Likewise, the intercept of the model which included the difference score between AU-regret and CU-regret was significant, indicating that CU was associated with lower levels of regret than AU alone (β = -0.3, SE = 0.10, t = -2.76, p = .01). A non-significant effect of Table 6. Stimulation as a Mediator in the Relationship Between Co-Use and Harms 95% CI Estimate (SD) LL UL Co-use Total Effect: co-use -0.26 (0.16) -0.29 0.29 Direct Effect: co-use 0.08 (0.15) 0.35 -0.15 Indirect Effect: number of drinks -0.3 (0.07) -0.3 -0.001* Indirect Effect: stimulation -0.03 (0.03) -0.03 0.07 Tendency to co-use Total Effect: co-use 1.4 (0.29) 0.66 1.72* Direct Effect: co-use 0.92 (0.23) 0.54 1.39* Indirect Effect: number of drinks 0.21 (0.13) -0.07 0.45 Indirect Effect: typical number of drinks 0.04 (0.01) 0.03 0.05* Indirect Effect: stimulation -0.03 (0.06) -0.18 0.08 Indirect Effect: typical stimulation 0.01 (0.01) -0.02 -0.07* Note. * = 95% CI does not overlap with 0. 62number of drinks was retained in the model for improving model fit (β =0.03, SE = 0.02, t = 1.43, p =.16). Secondary Aim: Co-use Predicting Intention Fulfilment Five linear multilevel models were built. Models included responses to prompts that inquired about the extent to which the drinking event achieved positive outcomes in these domains: enhancement, coping, social, expansion, and conformity. Each model was constructed within the subsample of participants that reported having intentions to engage in substance use for the purposes of achieving positive outcomes in these domains at the time before or during the drinking event. All five models are described in Table 7. Hypothesis 11: A significant positive effect of co-use was found in the domain of enhancement, thus confirming the prediction of H11 (β =0.32, SE = 0.16, t = 2, p =.04). Hypothesis 12: In line with predictions of H12, co-use was found to predict intention fulfilment in the domain of coping (β =1.19, SE = 0.55, t = 2.2, p =.04). Hypothesis 13: As expected by H13, co-use was related to intention fulfilment in the domain of social motivations. The final model indicated a significant interaction effect between co-use and number of drinks such that co-use was associated with greater social fulfilment at lower levels of drinking, but poorer social fulfilment at higher levels of drinking (β = -0.15, SE = 0.06, t = -2.57, p =.01; Figure 8). The main effect of co-use was not significant in the final model (β = 0.65, SE = 0.35, t = 1.88, p = 0.06). Hypothesis 14: In contrast to the predictions of H14, co-use was not found to be significantly related to intention fulfilment in the domain of expansion (β = -0.31, SE = 0.3, t = -1.06, p = 0.3). 63Hypothesis 15: Co-use was not found to be significantly related to intention fulfilment in the domain of conformity (β = 0.09, SE = 0.68, t = 0.13, p = 0.9), thus indicating that the predictions of H15 were not confirmed. Table 7 Co-use Predicting Intention Fulfilment Daily Enhancement Motivation (n = 163, N = 42) Daily Social Motivation (n = 153, N = 41) Daily Coping Motivation (n = 30, N = 15) β SE t β SE t β SE t (Intercept) 5.01*** (0.12) 40.58 3.19*** 0.24 13.04 3.7*** 0.44 8.41 Co-use 0.32 * (0.16) 2 0.65 0.35 1.86 1.2* 0.56 2.15 Number of drinks 0.19*** 0.05 4.275 Number of drinks X Co-use -0.153* 0.06 -2.57 Daily Conformity Motivation (n = 52, N = 25) Daily Expansion Motivation (n = 17, N = 13) β SE t β SE t (Intercept) 5.05*** 0.23 22.33 4.66*** 0.38 12.53 Co-use -0.32 0.3 -1.06 0.09 0.68 0.13 Note. Number of drinks X Co-use = the interaction between co-use and number of drinks,* p < 0.05, ** p < 0.01, *** p < 0.001 64 Figure 8. Interaction between Co-use and Number of drinks onto Daily Social Intention Fulfilment 65CHAPTER 4: Discussion The present study sought to enhance understanding of the nature and consequences relating to cannabis-alcohol co-use among young adults. Following a night of drinking, participants were asked to report on harms, regret, and the extent to which their drinking event achieved its desired outcome, such as “to be more social”. The relationship between co-use and harms depended on how much alcohol was consumed. Specifically, co-use was protective against harms in events where a participant had ten or more drinks. Additionally, the findings here indicate that the protective effect of co-use is overshadowed by the harmful effects of increased number of drinks that tend to accompany co-use events. These findings suggest that HED is safest in instances where co-use occurs without increased drinking. Further evidence for the relative safety of co-use emerged through investigation into the relationship between co-use and regret. Co-use was found to protect from feelings of regret in HED events relative to AU alone. These findings suggest that co-use while attending to drinking limits is the safest way to engage in HED. Approaching HED in this way allows for individuals to benefit from the protective effects of co-use onto harm and regret, while avoiding the risky influence of additional drinks. Moreover, results indicated that an additional benefit of co-use events is that they are more likely to fulfil enhancement, coping, and sociability intentions for substance use events motivated by these goals. The pattern of results here suggests that individuals engaging in HED can mitigate risk and promote positive outcomes by engaging in co-use without drinking more than they typically would if AU was used alone. Co-Use Relationship to Harm The findings of the present study align with emerging literature which suggest that the relationship between co-use and harmful consequences is complicated by the influence of 66possible third variable effects such as number of drinks. Without considering number of drinks, using cannabis in a night of drinking predicted greater harms, here, and in several other studies (Lee et al., 2020; Lipperman-Kreda, 2017; Mallet et al., 2019; Sokolovsky et al., 2020). Nonetheless, among our sample, number of drinks was found to significantly moderate the relationship between co-use and harms such that engaging in co-use differentially impacted harms across light drinking and HED events. Given this, the results of HED and light drinking will be discussed separately. Heavy Episodic Drinking Several notable findings emerged from the present study’s investigation into the relationship between co-use and harm at higher levels of number of drinks. First, results showed that co-use events are associated with a greater number of drinks relative to when AU is consumed in the absence of CU. Additionally, it was found that co-use is not more harmful when number of drinks is held constant. Finally, co-use protects from harms at extreme levels of drinking, such as ten or more drinks, where the harms of HED are most acute. The finding that co-use is associated with heavier drinking is surprising given the outcomes of previous studies that have suggested that co-use does not relate to a greater number of drinks consumed when operationalized with timing of CU considered (Rootman, 2020; Sokolovsky et al., 2020). Specifically, CU in a night of drinking was considered as co-use in the present study only when used before or during a night of drinking. One possible explanation for the discrepancy between this and earlier studies is a genuine shift in co-use practices among young adults in the several years between these studies. For example, population level studies have indicated overall fluctuations in the prevalence of CU, AU, and co-use among Canadians, and especially among college aged individuals (Statistics Canada, 2022; Hobin et al., 2023). 67Moreover, the legalization of recreational cannabis use has facilitated access to means of CU that were uncommon prior to legalization, such as portable electronic cannabis vaporizing pens and high THC concentrates (Statistics Canada, 2022). As such, it may be that the patterns of CU, AU, and co-use within the present study are distinct from earlier studies that found co-use was not associated with heavier drinking if used before or during a co-use event. Ongoing research that considers factors such as the regulatory context of CU, timing of substance use, and method of consumption, are necessary to understand whether, and how, CU relates to AU in a complementary or substitutive way. The association to greater number of drinks was found to underly the relationship between co-use and harms in HED events. Engaging in co-use predicts greater harms when number of drinks is not held constant across co-use and AU alone events. When drinks are kept equal across co-use and AU alone events, there is no longer a harmful association linked to co-use. This finding aligns with previous studies that have indicated that alcohol is the main contributor to risk within the relationship between co-use and harms (Lee et al., 2020; Lipperman-Kreda, 2017; Mallet et al., 2019; Sokolovsky et al., 2020). Moreover, the harmful influence of greater number of drinks overshadows the protective effects of co-use that emerge at extreme levels of drinking. The opposing influences of co-use and number of drinks has likely obscured the detection of co-use protective effects in previous investigations. Thus, although co-use tends to accompany heavier drinking, it appears that co-use itself is protective of harms. Given this, it seems that engaging in co-use is not necessarily a harmful practice relative to drinking alone, rather, the increased danger lies in a tendency for individuals to drink more within a night of co-use. 68 Protective effects of co-use at higher levels of drinking may be explained by the differences in quantity and quality of harms experienced across light and HED drinking events (Labhart, 2018). For example, within the present study, the average amount of harm that a participant experienced was more than doubled when considered within an HED event relative to a light drinking event. Thus, heavier drinking events present a greater opportunity for harm to be mitigated by any protective effects that co-use may exert. In addition, research suggests that individuals experience different sets of harms across light drinking and HED events. For example, harms such as vomiting and physical injury rarely emerge outside of HED events. As such, it is possible that co-use is protective of the harms specific to heavy drinking, whereas it exerts less of an effect onto harms incurred in light drinking events. Given that harms are experienced in greater quantities and among different types of negative outcomes across the spectrum of number of drinks, it follows that co-use would not necessarily be consistent in its impact on harm as drinking increases. There are psychological and environmental factors that differ between co-use events and AU events which may help to explain the mechanism by which co-use protects from harm at higher levels of drinking. For one, exploratory analysis in the present study suggested that co-use is associated with higher levels of the sedating effects of AU. This indicates that the psychoactive effects of co-use are distinct from when AU is consumed alone. Nonetheless, mediation analysis did not identify significant influence of these sedating effects in the association between co-use and harm. This null effect may be explained by the use of full sample data, including all observations regardless of their number of drinks, in this mediation analysis. Given that co-use was found to be protective at extreme levels of drinking, it follows that an indirect influence of sedation would be most potent at those extreme levels. Thus, the question 69remains unanswered whether sedation plays a role in this protective effect of co-use at extreme levels of drinking. In light of the association between co-use and sedation noted here, and known links between sedation and reduced harms (Quinn and Fromme, 2011), future research should aim to replicate this mediation analysis within a well-powered sample of extreme drinking observations. In summary, the present study closely examined the relationship between co-use and harm within the context of HED events and the factors that influence the extent to which co-use is harmful or protective. Findings are in line with previous studies (Mallet et al., 2019; Lee et al., 2020; Lipperman-Kreda, 2017; Sokolovsky et al., 2020) which indicate that engaging in co-use is not a harmful practice relative to drinking alone, instead, the increased danger lies in a tendency for individuals to drink more within a night of co-use. Moreover, the present study adds to the evidence suggesting that co-use exhibits a protective effect which emerges at high levels of drinking (Sokolovsky et al., 2020). Further research, especially within the context of extreme levels of number of drinks, is necessary to clarify when and how co-use is harmful or protective relative to AU alone. Light Drinking Events A different pattern of effects emerged within the context of light drinking events. Mediation analysis revealed that engaging in co-use within a drinking event was not related to increased harms. Instead, study findings indicate that the between-person effects of co-use are most germane to understanding how co-use relates to harm. When controlling for individual differences in tendency to co use, engaging in co-use was unrelated to experiencing harm in a given drinking event. These, between-person, tendency to co-use effects are distinct to findings at the HED level, wherein both the tendency to co-use and co-use were related to greater harms. 70The distinct set of factors that contribute to the experiencing of harms across light- and heavy-drinking strengthen the suggestion that co-use should be investigated separately across these contexts. These findings within the domain of light drinking suggest that co-use presents similar levels of harm as AU alone. This is noteworthy because, on their own, neither light drinking nor CU tend to be associated with substantial harms (Arterberry et al., 2021). Nonetheless, one concern of co-use is that the combination of these substances acts synergistically to produce greater harms than either substance in isolation. The findings of the present study indicate that is not the case: co-use is no more harmful than light drinking alone. This aligns with laboratory studies of co-use which have indicated the presence of synergistic psychoactive effects in HED, but not in light drinking (Lukas, & Orozco, 2001). Thus, one key takeaway from these findings is that co-use within a light drinking event can be a generally low risk behaviour: similar to light AU alone or CU without AU. The between-person effects within light drinking events highlight the importance of applying within-person methodology to a within-person effect such as engaging in co-use. A considerable portion of the literature examining the relationship between co-use and harm has been conducted cross-sectionally (Yurasek, 2017). With only one time point to examine, it is impossible to distinguish the effects related to engaging in co-use from the effects of being the type of person that tends to co-use. This is an important consideration as individual differences in substance use patterns are known to be associated with several confounding traits. For example, factors such as impulsivity, risk-taking tendencies, and sensation-seeking have been identified as higher among cannabis users (Connor et al., 2021). Thus, it is important that future studies of 71cannabis-alcohol co-use consider the role of both within- and between-person effects when investigating relationships between co-use and harm. Together, these findings suggest that cannabis-alcohol co-use within the context of light drinking can be considered as a relatively safe behavior, in line with prevailing views surrounding both light drinking and CU. The results also further illustrate the complexity within the relationship between co-use and harm. As such, it is critical that future investigations implement advanced methodologies and theoretically driven analyses to explore the dynamics between co-use, number of drinks, and harms. Co-use Relationship to Regret Beyond the primary aim of the present study, which sought to examine how co-use relates to harms, secondary analyses were conducted to explore the relationship between co-use and other relevant outcomes. Within co-use events, participants tended to endorse lower levels of regret related to their decision to engage in CU relative to their choice to drink alcohol. Additionally, across all drinking events, co-use was associated with less regretful drinking relative to when AU was used without CU. Finally, co-use was found to be associated with greater intention fulfilment within the domains of enhancement, coping, and socializing. These results add further evidence to the idea that, in contrast to the negative outcomes associated with AU, co-use may be protective of negative outcomes in some contexts. Though limited, available research examining regret and across CU and AU can help to explain the findings of the present study. For example, the average person reports regret about getting drunk in one third of their drinking occasions (Winstock, 2019), whereas regret over a single occasion of CU appears to be relatively rare (Palamar et al., 2018). The findings in the present study extend this body of research to suggest that CU remains less regretful than AU 72even when the two substances are co-used. In addition, co-use provides an opportunity to directly compare CU and AU in identical circumstances (e.g., mood, environment, individual factors) that is not otherwise possible when the substances are used separately. This comparison is not exact, given that the experience of co-use likely exerts influence onto CU and AU regret that is not present when the substances are used separately. Nonetheless, the controlled comparison of the two substances within co-use events offers a useful extension to previous studies that have compared regret associated with CU and AU (Palamar et al., 2018). As with harm, there is a concern that co-use may increase regret via a synergistic effect wherein the combination of CU and AU increases regrettable behaviour. However, the findings of the present study suggest the opposite: co-use is associated with reduced regret over drinking. One possible explanation for this finding is that, relative to harm, regret is more sensitive to the protective effects of co-use. As such, it may be that the protective effects of co-use found for harms at ten or more drinks could be identified at less extreme levels when regret is considered as the outcome. Regret captures the perceived weight of harms such that, even when a lower objective count of harms are experienced, these harms can be weighted as highly regretful in retrospect. Another reason that regret may be more sensitive to the protective effects of co-use is that regret may be evoked in circumstances where there is a misbalance in costs and benefits. For example, even when no harms occur, an individual may reflect regretfully on a drinking occasion if it feels like it was a “waste” due to the time, financial, and effort costs experienced without any beneficial gain. Thus, findings related to regret may offer a more holistic characterization of co-use that can account for the balance of weighted subjective negative and positive outcomes. 73Co-use Relationship to Intention Fulfilment This understanding of regret is congruent with present study observations that co-use is more strongly associated with intention fulfilment in the domains of enhancement, socialization, and coping. Specifically, it is possible that co-use promotes positive-, and mitigates negative-, outcomes in these domains, which may contribute to a less regretful balance of costs and benefits relative to when AU is used without CU. For example, an individual who enjoyed the effects of their substance use and found it to help with sociability, may be less likely to consider a hangover to be regretful relative to if enhancement and social intentions were not achieved. As such, it may be that co-use mitigates regret by way of increasing positive outcomes in some domains. The finding that co-use is associated with increased social intention fulfilment at lower levels of drinking but reduced social fulfilment at higher levels of drinking is noteworthy. This may be related to the higher levels of sedation found to be associated with co-use. Specifically, gregariousness and other social effects are associated with the stimulating effects of AU, whereas sedation may mitigate the presence of these effects if it overwhelms stimulation (Rueger & King., 2013). Given that the balance of effects is weighted towards sedation at higher levels of drinking, it may be that the amplification of these effects further mitigates the social benefits of AU. At lower levels of drinking CU may increase positive outcomes through anxiolytic- or stimulating-effects of CU (Green., 2003) that are muted by sedation at higher levels of drinking. The findings here suggests that co-use may facilitate positive social outcomes at low levels of drinking, have a neutral effect on social outcomes at moderate levels, and interfere with outcomes at high levels of drinking. 74Implications To date, public messaging and policy relating to co-use of cannabis and alcohol has been driven by the understanding that combined use presents greater risk for short-term consequences relative to the use of either substance alone (Canadian Nurses Association, 2017; Cochrane Canada, 2021; Delphi Behavioral Health Group, 2019; HealthLinkBc, 2019). This perspective is reflected in current harm-reduction materials which often broadly discourage co-use and policy intended to prevent co-use with the intention of reducing harm. For example, the sale of cannabis within liquor stores is prohibited across all provinces and territories except for Nova Scotia. The conceptualization of co-use as a generally harmful behaviour is likely rooted in early research on the topic (Yurasek, 2017), and moreover, potentially influenced by societal views on CU that have been negatively biased through stigma generated by the now debunked war-on-drugs (Reid., 2020). Given the marked global burden of AU (CCSA, 2017; Brown et al., 2021) and widespread increases in CU prevalence (Statistics Canada., 2022), it is critical that harm reduction initiatives are grounded in current empirical evidence. The findings from the present study and other emerging research align in suggesting that co-use is no more harmful than AU alone, and at extreme levels of drinking, may be harm reducing (Mallet et al., 2019; Lee et al., 2020; Lipperman-Kreda, 2017; Sokolovsky et al., 2020). Co-use tends to accompany greater number of drinks, but when separated from the harmful influence of higher number of drinks, it is protective of harms at extreme levels of drinking. Further, it helps to contextualize past research as insufficient given historical methodological limitations, and therefore, highlights the importance of modern research in understanding co-use and its outcomes. As such, it is time to reconsider policy and harm-reduction approaches designed to broadly discourage co-use. Adaptations to these initiatives that derive from 75conceptualizations of co-use as, at minimum, no more harmful than AU alone are well warranted given the wealth of accumulating evidence in support of this perspective. Policies directed at co-use mitigation appear to be unnecessarily restrictive, whereas information suggesting co-use is more harmful than AU may mislead and breed mistrust among intended audiences (Krishna & Thompson, 2021) These findings suggest multiple avenues by which co-use may protect against negative-, or promote positive-, outcomes in the following contexts: harm at extreme levels of AU, regret within HED, and achievement of substance use intentions related to enhancement, coping, and socializing. Pending further investigation to confirm the findings of the present study, consideration of these putative positive effects may also serve to enhance harm reduction initiatives. Taken together, there is an incongruency between contemporary scientific perspectives and current harm-reduction initiatives relating to the topic of co-use. Efforts to bridge this gap can be an important contribution to ongoing movements made to mitigate the substantial harms associated with AU and especially HED. The implications discussed here are especially salient given the vulnerability of young adults to the harms of HED. Typically, HED is associated with greater harm than light drinking, and this effect is amplified with each drink consumed (Taylor et al., 2010). Moreover, compared to the broader population, young adults tend to show a higher prevalence of HED, greater harms associated with HED, and generally engage in HED within riskier environments (CCSA, 2017). Thus, HED among young adults represents a context where the need for empirically directed harm reduction initiatives is especially salient. The findings of the present study and others (Mallet et al., 2019; Lee et al., 2020; Lipperman-Kreda, 2017; Sokolovsky et al., 2020) provide clear guidance in the endeavour of revising these initiatives: co-use should not be characterized 76as more harmful than AU alone. Instead, harm reduction initiatives should target number of drinks in the design of their interventions. Recommendations which broadly advise against co-use with the intent of preventing harm should be shifted towards a more balanced view of co-use (Canadian Nurses Association, 2017; Cochrane Canada, 2021; Delphi Behavioral Health Group, 2019; HealthLinkBC, 2019). For example, harm reduction recommendations to individuals engaging in HED could remind individuals to observe their drinking limits when co-using. Recommendations with this balanced perspective better align with contemporary research on the topic. Moreover, the perspective that co-use is not inherently dangerous is better centered around the lived experiences of people who co-use. These individuals likely have a good understanding of how co-use tends to relate to harm based in their own history of use. Creating guidelines that are congruent with these experiences is necessary for people to meaningfully integrate recommendations into their lives. Given the vulnerability of young adults to HED harms, such revisions are critically important. Strengths, Limitations, and Future Directions The present study employed EMA methodology as a means of exploring the nuanced relationship between co-use, number of drinks, harms, and other relevant variables. This approach is a necessary advancement beyond other methods, such as cross-sectional or population-level studies, which cannot distinguish within-person effects. The use of EMA methodology also provides the opportunity to investigate this topic in a real-world context, and as such, provides a helpful parallel perspective to laboratory-based studies which have limited ecological validity (Lukas, & Orozco, 2001). Further, the population of young adults studied here represent a context where questions related to co-use and negative outcomes are of particular importance given the high risk for drinking harms in this group. 77Several limitations exist alongside these relative study strengths. For one, responses relating to co-use, number of drinks, and all outcomes of the present study were collected the day following the drinking event. Recall may have been biased in these responses, especially because previous studies have found that estimates of number of drinks tend to lower as time passes between the drinking event and the time of response (Gmel, & Daeppen, 2007). As such, although the use of EMA methodology in the present study is an advancement over other methods of retrospective recall, such as the Timeline Follow Back Technique (Sobell and Sobell, 1998) which may occur days or weeks after drinking, the effect of recall bias may have still influenced the observed results. Similarly, the longitudinal nature of the study is a notable strength, but it necessitated year-round data collection, which may have influenced study findings. Specifically, there are known associations between season of year and substance use patterns (Cho et al., 2001; Lemmens & Knibbe, 1993; Knudsen & Skogen, 2015) which were not accounted for in analysis. Additionally, the generalizability of findings in the present study are limited by the specificity of our sample to college aged students. Indeed, although young adults represent a relevant context for this investigation, it is unclear whether the findings of the present study generalize to other populations. Additionally, our sample of young adults were recruited from a university sample, and therefore further research is needed to confirm the replication of our findings among all young adults. Nonetheless, supportive evidence of AU as the riskier behaviour within co-use events has been noted across disparate groups such as veterans (Metrik et al., 2018) and adolescents (Lee et al., 2020). Thus, it appears that this primary finding within the present study of equal harm associated with AU and co-use is robust across populations. This helps to provide some confirmation that co-use effects, including novel secondary findings such 78as putative protective effects and associations to outcomes outside of harm, may hold true among different populations. Another limiting factor to generalizability in the present study was lack of sufficient data on outcomes across the complete spectrum of number of drinks. Although the collection of adequate heavy drinking data to conduct fine-grained analyses within the present study represents an advancement to earlier studies, there were several analyses at various levels of drinking that were restricted due to power. For example, HED in the present study was defined as 4-5 or more drinks which may have obscured findings unique to extreme levels of drinking such as 10 or more drinks. There is a growing body of literature which suggests these extreme levels of drinking represent a distinct category of HED such that the nature of drinking events is best understood as unique across these three categories as opposed to the dichotomy of light- versus heavy-drinking investigated here (Labhart, 2018; White et al., 2016). The finding that co-use may be protective at these extreme levels of drinking highlights the likely existence of this third category. Likewise, several analyses conducted on subsamples of data, such as comparisons of CU and AU regret limited to only co-use events, could not be investigated separately across light- and heavy-drinking. As such, there is potential that findings have been complicated by differential effects across these levels. Thus, although the present study represents a methodological progression through investigation into the distinct nature of co-use across various levels of drinking, it was also limited in the extent to which this could be applied to all analyses and levels of number of drinks. Beyond number of drinks, the present study was limited in the specificity of its CU data. Participants reported whether or not they engaged in CU, but there was no data gathered on the type of cannabis used and the mode of CU. Given evidence suggesting that these factors relate to 79several relevant outcomes such as craving and intoxication (Paulus et al., 2022), it is possible that they have contributed to the findings discussed here. In line with these limitations, future investigations can extend the findings of the present study in several ways. For one, replication of the present study’s findings to the general population and subgroups at risk for HED related harms is needed to understand the wider applicability of these results. Additionally, replication within the context of extreme drinking is necessary to clarify the nature of effects at that level. For example, the mechanisms that underly any protective effects of co-use onto harm are unclear but may relate to the heightened presence of AU sedation following co-use or other factors not investigated in the present study. This is especially relevant given that extreme drinking has the strongest relationship to short-term harmful consequences and negative outcomes. Thus, enhanced understanding- and replication of- co-use protective effects at extreme levels of drinking represent an impactful next step for this area of research. Finally, findings related to regret and intention fulfilment are novel, and further exploration into their nature and relationship to relevant outcomes, such as future drinking or co-use behaviour, is warranted. Conclusion Cannabis-alcohol co-use is becoming an increasingly common behaviour among Canadians, and recent literature has suggested that it is relatively safe compared to the alternative of using alcohol without cannabis. This line of emerging research contradicts contemporary public messaging and policy which often holds the perspective that combined use presents greater risk for short-term consequences relative to the use of alcohol alone. The findings of the present study support the characterisation that cannabis-alcohol co-use does not add harms onto of AU-alone events. Rather, it is likely that AU behaviours, such as additional number of drinks, 80are the driving force behind negatively consequential drinking experiences. Further, the present study highlighted several ways that co-use within a night of drinking may be potentially beneficial or protective. For example, co-use was noted to be associated with lower levels of regret about drinking and greater intention fulfilment relative to alcohol-alone. Additionally, co-use became progressively more protective of short-term harms as number of drinks increased within a night of HED. In contrast to historical understanding of co-use within public messaging and policy directives, this pattern of results strongly argues that co-use is not a fundamentally harmful behaviour. Instead, it appears that combining cannabis onto a night of drinking is a relatively benign decision with some potential avenues by which it may increase safety or benefit. Further research is warranted to clarify the nature of these putative positive outcomes of co-use, but nonetheless, the present study highlights a salient incongruency between research and policy. Given the substantial harms associated with alcohol use, it is essential that this apparent gap between empirical research and public approaches to harm-reduction is mitigated. Revised directives should consider co-use as, at most, neutral, pending further investigation into putative benefits and further clarification of its relationship to harm. 81References Anton, R. F. (2008). Naltrexone for the management of alcohol dependence. New England Journal of Medicine, 359(7), 715-721. Arend, M. G., & Schäfer, T. (2019). Statistical power in two-level models: A tutorial based on Monte Carlo simulation. Psychological methods, 24(1), 1. Barrett, S. P., Darredeau, C., & Pihl, R. O. (2006). Patterns of simultaneous polysubstance use in drug using university students. Human Psychopharmacology: Clinical and Experimental, 21, 255-263. https://doi.org/10.1002/hup.766 Ballard, M. E., & de Wit, H. (2011). Combined effects of acute, very-low-dose ethanol and delta (9)-tetrahydrocannabinol in healthy human volunteers. Pharmacology Biochemistry and Behavior, 97(4), 627-631. Baylen, C. A., & Rosenberg, H. (2006). A review of the acute subjective effects of MDMA/ecstasy. Addiction, 101(7), 933-947. Bershad, A. K., Miller, M. A., Baggott, M. J., & de Wit, H. (2016). The effects of MDMA on socio-emotional processing: does MDMA differ from other stimulants?. Journal of Psychopharmacology, 30(12), 1248-1258. Briasoulis, A., Agarwal, V., & Messerli, F. H. (2012). Alcohol consumption and the risk of hypertension in men and women: a systematic review and meta-analysis. The Journal of Clinical Hypertension, 14, 792-798. https://doi.org/10.1111/jch.12008 British Columbia Liquor and Cannabis Regulation Brach (2019b). Cannabis Information for Liquor Licensees and Permittees. Author 82Brown, S.W., Vanlaar, W.G.M., and Robertson, R.D. (2021). The Alcohol-Crash Problem in Canada: 2016. Ottawa, Ontario: Transport Canada, Road Safety and Motor Vehicle Regulation. Brunt, T. M., van Genugten, M., Höner-Snoeken, K., van de Velde, M. J., & Niesink, R. J. (2014). Therapeutic satisfaction and subjective effects of different strains of pharmaceutical-grade cannabis. Journal of clinical psychopharmacology, 34(3), 344-349. Butt, P., Beirness, D., Gliksman, L., Paradis, C., & Stockwell, T. (2011). Alcohol and health in Canada: a summary of evidence and guidelines for low-risk drinking. Ottawa, ON: Canadian Centre on Substance Abuse Boyle, H. K., Gunn, R. L., López, G., Fox, O. S., & Merrill, J. E. (2021). Qualitative examination of simultaneous alcohol and cannabis use reasons, evaluations, and patterns among heavy drinking young adults. Psychology of Addictive Behaviors. Canadian Centre on Substance Abuse and Addiction. (2017). Canadian drug summary: Alcohol. Ottawa, ON. Canadian Nurses Association. (2018). Harm Reduction for Non-Medical Cannabis Use. Ottawa, ON Canadian Substance Use Costs and Harms Scientific Working Group. (2020). Canadian substance use costs and harms 2015–2017. (Prepared by the Canadian Institute for Substance Use Research and the Canadian Centre on Substance Use and Addiction.) Ottawa, Ont.: Canadian Centre on Substance Use and Addiction. Chait, L. D., & Perry, J. L. (1994). Acute and residual effects of alcohol and marijuana, alone and in combination, on mood and performance. Psychopharmacology, 115(3), 340-349. Cho, Y. I., Johnson, T. P., & Fendrich, M. (2001). Monthly variations in self-reports of alcohol consumption. Journal of studies on alcohol, 62(2), 268-272. 83Cochrane Canada. (2021). Update of Canada’s Low-Risk Alcohol Drinking Guidelines: Summary of Evidence on Understanding and Response to Alcohol Consumption Guidelines. Ottawa, Ont.: Canadian Centre on Substance Use and Addiction. Delphi Behavioral Health Group. (2019). Alcoholism and Drug Use - A Dangerous Combination. Retrieved from https://www.alcoholrehabguide.org/alcohol/drinking-anddrugs/. Retrieved on: May 11th 2019 DiNardo, J., & Lemieux, T. (2001). Alcohol, marijuana, and American youth: the unintended consequences of government regulation. Journal of health economics, 20, 991-1010. https://doi.org/10.1016/S0167-6296(01)00102-3 Eckhardt, C. I., Parrott, D. J., & Sprunger, J. G. (2015). Mechanisms of alcohol-facilitated intimate partner violence. Violence against women, 21(8), 939-957. El Ansari, W., Salam, A., & Suominen, S. (2020). Is alcohol consumption associated with poor perceived academic performance? Survey of undergraduates in Finland. International journal of environmental research and public health, 17(4), 1369. Fareed, A., Vayalapalli, S., Stout, S., Casarella, J., Drexler, K., & Bailey, S. P. (2011). Effect of methadone maintenance treatment on heroin craving, a literature review. Journal of Addictive Diseases, 30(1), 27-38. https://doi.org/10.1080/10550887.2010.531672 Fischman, M. W., Schuster, C. R., Resnekov, L., Shick, J. F. E., Krasnegor, N. A., Fennell, W., & Freedman, D. X. (1976). Cardiovascular and subjective effects of intravenous cocaine administration in humans. Archives of General Psychiatry, 33(8), 983-989. Gmel, G., & Daeppen, J. B. (2007). Recall bias for seven-day recall measurement of alcohol consumption among emergency department patients: implications for case-crossover designs. Journal of studies on alcohol and drugs, 68(2), 303-310. 84Green, B. O. B., Kavanagh, D., & Young, R. (2003). Being stoned: a review of self‐reported cannabis effects. Drug and alcohol review, 22(4), 453-460. Green, K. M., Musci, R. J., Johnson, R. M., Matson, P. A., Reboussin, B. A., & Ialongo, N. S. (2016). Outcomes associated with adolescent marijuana and alcohol use among urban young adults: A prospective study. Addictive behaviors, 53, 155-160. Gunn, R. L., Norris, A. L., Sokolovsky, A., Micalizzi, L., Merrill, J. E., & Barnett, N. P. (2018). Marijuana use is associated with alcohol use and consequences across the first 2 years of college. Psychology of Addictive Behaviors, 32(8), 885. https://doi.org/10.1037/adb0000416 Gunn, R., Jackson, K., Borsari, B., & Metrik, J. (2019). A longitudinal examination of daily patterns of cannabis and alcohol co-use among medicinal and recreational veteran cannabis users. Drug and alcohol dependence, 205, 107661. Gunn, R. L., Sokolovsky, A., Stevens, A. K., Hayes, K., Fitzpatrick, S., White, H. R., & Jackson, K. M. (2021). Contextual influences on simultaneous alcohol and cannabis use in a predominately white sample of college students. Psychology of addictive behaviors. Hart, C. L., Van Gorp, W., Haney, M., Foltin, R. W., & Fischman, M. W. (2001). Effects of acute smoked marijuana on complex cognitive performance. Neuropsychopharmacology, 25(5), 757-765. Hart, C. L., Ward, A. S., Haney, M., Comer, S. D., Foltin, R. W., & Fischman, M. W. (2002). Comparison of smoked marijuana and oral Δ 9-tetrahydrocannabinol in humans. Psychopharmacology, 164(4), 407-415. 85Hartman, R. L., Brown, T. L., Milavetz, G., Spurgin, A., Pierce, R. S., Gorelick, D. A., ... & Huestis, M. A. (2015). Cannabis effects on driving lateral control with and without alcohol. Drug and alcohol dependence, 154, 25-37. Hartman, R. L., Brown, T. L., Milavetz, G., Spurgin, A., Pierce, R. S., Gorelick, D. A., ... & Huestis, M. A. (2016). Cannabis effects on driving longitudinal control with and without alcohol. Journal of applied toxicology, 36(11), 1418-1429. Hurlbut, S. C., & Sher, K. J. (1992). Assessing alcohol problems in college students. Journal of American College Health, 41(2), 49-58. King, A. C., McNamara, P. J., Hasin, D. S., & Cao, D. (2014). Alcohol challenge responses predict future alcohol use disorder symptoms: a 6-year prospective study. Biological psychiatry, 75(10), 798-806. Knudsen, A. K., & Skogen, J. C. (2015). Monthly variations in self-report of time-specified and typical alcohol use: The Nord-Trøndelag Health Study (HUNT3). BMC Public Health, 15, 1-11. Ray, L. A., Green, R., Roche, D. J., Magill, M., & Bujarski, S. (2019). Naltrexone effects on subjective responses to alcohol in the human laboratory: A systematic review and meta‐analysis. Addiction biology, 24(6), 1138-1152. Read, J. P., Kahler, C. W., Strong, D. R., & Colder, C. R. (2006). Development and preliminary validation of the young adult alcohol consequences questionnaire. Journal of studies on alcohol, 67(1), 169-177. Reus, V. I., Fochtmann, L. J., Bukstein, O., Eyler, A. E., Hilty, D. M., Horvitz-Lennon, M., … & McIntyre, J. (2018). The American Psychiatric Association Practice Guideline for the 86Pharmacological Treatment of Patients With Alcohol Use Disorder. American Journal of Psychiatry, 175(1), 86-90. Hicks, J. A., Fields, S., Davis, W. E., & Gable, P. A. (2015). Heavy drinking, impulsivity and attentional narrowing following alcohol cue exposure. Psychopharmacology, 232(15), 2773-2779. HealthLinkBC. (2019, October 17). Cannabis. Retrieved from www.healthlinkbc.ca/hlbc/files/documents/healthfeature/cannabis.pdf. Hursh, S. R., Galuska, C. M., Winger, G., & Woods, J. H. (2005). The economics of drug abuse: a quantitative assessment of drug demand. Molecular interventions, 5(1), 20. https://doi.org/10.1124/mi.5.1.6 Kalman, D. (2002). The subjective effects of nicotine: methodological issues, a review of experimental studies, and recommendations for future research. Nicotine & Tobacco Research, 4(1), 25-70. Kleinloog, D., Roozen, F., De Winter, W., Freijer, J., & Van Gerven, J. (2014). Profiling the subjective effects of Δ9‐tetrahydrocannabinol using visual analogue scales. International journal of methods in psychiatric research, 23(2), 245-256. Lee, C. M., Patrick, M. E., Fleming, C. B., Cadigan, J. M., Abdallah, D. A., Fairlie, A. M., & Larimer, M. E. (2020). A Daily Study Comparing Alcohol-Related Positive and Negative Consequences for Days With Only Alcohol Use Versus Days With Simultaneous Alcohol and Marijuana Use in a Community Sample of Young Adults. Alcoholism: Clinical and Experimental Research 87Leeman, R. F., Palmer, R. S., Corbin, W. R., Romano, D. M., Meandzija, B., & O'Malley, S. S. (2008). A pilot study of naltrexone and BASICS for heavy drinking young adults. Addictive behaviors, 33(8), 1048-1054. Lemmens, P. H., & Knibbe, R. A. (1993). Seasonal variation in survey and sales estimates of alcohol consumption. Journal of studies on alcohol, 54(2), 157-163. Linden-Carmichael, A. N., Van Doren, N., Masters, L. D., & Lanza, S. T. (2020). Simultaneous alcohol and marijuana use in daily life: Implications for level of use, subjective intoxication, and positive and negative consequences. Psychology of addictive behaviors Lipperman-Kreda, S., Gruenewald, P. J., Grube, J. W., & Bersamin, M. (2017). Adolescents, alcohol, and marijuana: Context characteristics and problems associated with simultaneous use. Drug and alcohol dependence, 179, 55-60 Looby, A., Prince, M. A., Villarosa-Hurlocker, M. C., Conner, B. T., Schepis, T. S., Bravo, A. J., & Stimulant Norms and Prevalence (SNAP) Study Team. (2021). Young adult use, dual use, and simultaneous use of alcohol and marijuana: An examination of differences across use status on marijuana use context, rates, and consequences. Psychology of Addictive Behavior, 35(6), 682-690 Lucas, P., Baron, E. P., & Jikomes, N. (2019). Medical cannabis patterns of use and substitution for opioids & other pharmaceutical drugs, alcohol, tobacco, and illicit substances; results from a cross-sectional survey of authorized patients. Harm reduction journal, 16(1), 9. https://doi.org/10.1186/s12954-019-0278-6 Lucas, P., & Walsh, Z. (2017). Medical cannabis access, use, and substitution for prescription opioids and other substances: A survey of authorized medical cannabis patients. 88International Journal of Drug Policy, 42, 30-35. https://doi.org/10.1016/j.drugpo.2017.01.011 Lucas, P., Boyd, S., Milloy, M. J., & Walsh, Z. (2020). Reductions in alcohol use following medical cannabis initiation: results from a large cross-sectional survey of medical cannabis patients in Canada. International Journal of Drug Policy, 86, 102963. Lukas, S. E., & Orozco, S. (2001). Ethanol increases plasma Δ9-tetrahydrocannabinol (THC) levels and subjective effects after marihuana smoking in human volunteers. Drug and alcohol dependence, 64(2), 143-149. MacKinnon, D. P., & Luecken, L. J. (2008). How and for whom? Mediation and moderation in health psychology. Health psychology, 27(2S), S99. Magill, M., Barnett, N. P., Apodaca, T. R., Rohsenow, D. J., & Monti, P. M. (2009). The role of marijuana use in brief motivational intervention with young adult drinkers treated in an emergency department. Journal of Studies on Alcohol and Drugs, 70(3), 409-413. Mallett, K. A., Turrisi, R., Trager, B. M., Sell, N., & Linden-Carmichael, A. N. (2019). An examination of consequences among college student drinkers on occasions involving alcohol-only, marijuana-only, or combined alcohol and marijuana use. Psychology of addictive behaviors: journal of the Society of Psychologists in Addictive Behaviors, 33(3), 331-336. https://doi.org/10.1037/adb0000458 Martin, C. S., Earleywine, M., Musty, R. E., Perrine, M. W., & Swift, R. M. (1993). Development and validation of the biphasic alcohol effects scale. Alcoholism: Clinical and Experimental Research, 17(1), 140-146. McKetin, R., Chalmers, J., Sunderland, M., & Bright, D. A. (2014). Recreational drug use and binge drinking: Stimulant but not cannabis intoxication is associated with excessive 89alcohol consumption. Drug and Alcohol Review, 33(4), 436–445. https://doi.org/10.1111/dar.12147 Metrik, J., Gunn, R. L., Jackson, K. M., Sokolovsky, A. W., & Borsari, B. (2018). Daily patterns of marijuana and alcohol co-use among individuals with alcohol and cannabis use disorders. Alcoholism: Clinical and Experimental Research, 42(6), 1096-1104 Midanik, L. T., Tam, T. W., & Weisner, C. (2007). Concurrent and simultaneous drug and alcohol use: results of the 2000 National Alcohol Survey. Drug and alcohol dependence, 90(1), 72-80. Mikuriya, T. H. (1970). Cannabis substitution. An adjunctive therapeutic tool in the treatment of alcoholism. Med Times, 98(4), 187-91. Morean, M. E., Corbin, W. R., & Treat, T. A. (2013). The Subjective Effects of Alcohol Scale: development and psychometric evaluation of a novel assessment tool for measuring subjective response to alcohol. Psychological assessment, 25(3), 780. Morgenstern, J., Kuerbis, A. N., Chen, A. C., Kahler, C. W., Bux Jr, D. A., & Kranzler, H. R. (2012). A randomized clinical trial of naltrexone and behavioral therapy for problem drinking men who have sex with men. Journal of consulting and clinical psychology, 80(5), 863. Morris, D. H., Treloar, H., Tsai, C. L., McCarty, K. N., & McCarthy, D. M. (2016). Acute subjective response to alcohol as a function of reward and punishment sensitivity. Addictive behaviors, 60, 90-96. Mota, N., Álvarez-Gil, R., Corral, M., Rodríguez Holguín, S., Parada, M., Crego, A., ... & Cadaveira, F. (2010). Risky alcohol use and heavy episodic drinking among Spanish University students: a two-year follow-up. Gaceta sanitaria, 24, 372-377. 90Murphy, S. M., Jeng, P. J., McCollister, K. E., Leff, J. A., Jalali, A., Shulman, M., ... & Schackman, B. R. (2021). Cost-effectiveness implications of increasing the efficiency of the extended-release naltrexone induction process for the treatment of opioid use disorder: a secondary analysis. Addiction. Newlin, D. B., & Thomson, J. B. (1990). Alcohol challenge with sons of alcoholics: a critical review and analysis. Psychological bulletin, 108(3), 383. O'Hara, R. E., Armeli, S., & Tennen, H. (2016). Alcohol and cannabis use among college students: Substitutes or complements?. Addictive behaviors, 58, 1-6. https://doi.org/10.1016/j.addbeh.2016.02.004 Roche, D. J. O., Bujarski, S., Green, R., Hartwell, E. E., Leventhal, A. M., & Ray, L. A. (2019). Alcohol, tobacco, and marijuana consumption is associated with increased odds of sameday substance co-and tri-use. Drug and alcohol dependence, 200, 40-49. Pacek, L. R., Martins, S. S., & Crum, R. M. (2013). The bidirectional relationships between alcohol, cannabis, co-occurring alcohol and cannabis use disorders with major depressive disorder: results from a national sample. Journal of affective disorders, 148(2-3), 188-195. Patrick, M. E., Fleming, C. B., Fairlie, A. M., & Lee, C. M. (2020). Cross-fading motives for simultaneous alcohol and marijuana use: Associations with young adults’ use and consequences across days. Drug and alcohol dependence, 213, 108077. Patrick, M. E., & Lee, C. M. (2018). Cross-faded: Young adults’ language of being simultaneously drunk and high. Cannabis (Research Society on Marijuana), 1(2), 60. , Paulus, V., Billieux, J., Benyamina, A., & Karila, L. (2022). Cannabidiol in the context of substance use disorder treatment: A systematic review. Addictive behaviors, 132, 107360. 91Piasecki, T. M., Jahng, S., Wood, P. K., Robertson, B. M., Epler, A. J., Cronk, N. J., ... & Sher, K. J. (2011). The subjective effects of alcohol–tobacco co-use: An ecological momentary assessment investigation. Journal of abnormal psychology, 120(3), 557. Piazza-Gardner, A. K., Barry, A. E., & Merianos, A. L. (2016). Assessing drinking and academic performance among a nationally representative sample of college students. Journal of Drug Issues, 46(4), 347-353. Porter, S. R., & Pryor, J. (2007). The effects of heavy episodic alcohol use on student engagement, academic performance, and time use. Journal of College Student Development, 48(4), 455-467. Price Wolf, J., Lipperman-Kreda, S., & Bersamin, M. (2019). “It just depends on the environment”: Patterns and decisions of substance use and co-use by adolescents. Journal of child & adolescent substance abuse, 28(3), 143-149. Ramaekers, J. G., Robbe, H. W. J., & O'Hanlon, J. F. (2000). Marijuana, alcohol and actual driving performance. Human Psychopharmacology: Clinical and Experimental, 15(7), 551-558. Ramaekers, J. G., Theunissen, E. L., De Brouwer, M., Toennes, S. W., Moeller, M. R., & Kauert, G. (2011). Tolerance and cross-tolerance to neurocognitive effects of THC and alcohol in heavy cannabis users. Psychopharmacology, 214(2), 391-401. Ray, L. A., MacKillop, J., Leventhal, A., & Hutchison, K. E. (2009). Catching the alcohol buzz: an examination of the latent factor structure of subjective intoxication. Alcoholism: Clinical and Experimental Research, 33(12), 2154-2161. Ray, L. A., Bujarski, S., & Roche, D. J. (2016). Subjective response to alcohol as a research domain criterion. Alcoholism: Clinical and Experimental Research, 40(1), 6-17. 92Rehm, J., Taylor, B., Mohapatra, S., Irving, H., Baliunas, D., Patra, J., & Roerecke, M. (2010). Alcohol as a risk factor for liver cirrhosis: A systematic review and meta-analysis. Drug and alcohol review, 29(4), 437-445. https://doi.org/10.1111/j.1465-3362.2009.00153.x Reiman, A. (2009). Cannabis as a substitute for alcohol and other drugs. Harm Reduction Journal, 6(1), 35. https://doi.org/10.1186/1477-7517-6-35 Risso, C., Boniface, S., Subbaraman, M. S., & Englund, A. (2020). Does cannabis complement or substitute alcohol consumption? A systematic review of human and animal studies. Journal of Psychopharmacology, 34(9), 938-954. Roche, D. J. O., Bujarski, S., Green, R., Hartwell, E. E., Leventhal, A. M., & Ray, L. A. (2019). Alcohol, tobacco, and marijuana consumption is associated with increased odds of sameday substance co-and tri-use. Drug and alcohol dependence, 200, 40-49. Rootman, J. M. (2020). The impact of cannabis-alcohol co-use on drink consumption: timing is key (Masters Thesis, University of British Columbia). Rueger, S. Y., & King, A. C. (2013). Validation of the Brief Biphasic Alcohol Effects Scale (B‐BAES). Alcoholism: Clinical and Experimental Research, 37(3), 470-476. Siegel, S. (2001). Pavlovian conditioning and drug overdose: When tolerance fails. Addiction Research & Theory, 9(5), 503-513. Schacht, J. P., Selling, R. E., & Hutchison, K. E. (2009). Intermediate cannabis dependence phenotypes and the FAAH C385A variant: an exploratory analysis. Psychopharmacology, 203(3), 511-517. Schlienz, N. J., & Lee, D. C. (2018). Co-use of cannabis, tobacco, and alcohol during adolescence: policy and regulatory implications. International review of psychiatry, 30(3), 226-237. 93Schmid, Y., Gasser, P., Oehen, P., & Liechti, M. E. (2021). Acute subjective effects in LSD-and MDMA-assisted psychotherapy. Journal of Psychopharmacology, 35(4), 362-374. Schuckit, M. A., Smith, T. L., Kalmijn, J., Tsuang, J., Hesselbrock, V., & Bucholz, K. (2000). Response to alcohol in daughters of alcoholics: a pilot study and a comparison with sons of alcoholics. Alcohol and Alcoholism, 35(3), 242-248. Schuckit, M. A. (2009). Alcohol-use disorders. The Lancet, 373(9662), 492-501. Shiffman S. (2009). Ecological momentary assessment (EMA) in studies of substance use. Psychological assessment, 21(4), 486–497. https://doi.org/10.1037/a0017074 Shillington, A. M., & Clapp, J. D. (2001). Substance use problems reported by college students: Combined marijuana and alcohol use versus alcohol-only use. Substance use & misuse, 36(5), 663-672. Shillington, A. M., & Clapp, J. D. (2006). Heavy alcohol use compared to alcohol and marijuana use: Do college students experience a difference in substance use problems?. Journal of Drug Education, 36(1), 91-103. https://doi.org/10.2190/8PRJ-P8AJ-MXU3-H1MW Sobell, L. C., & Sobell, M. B. (1992). Timeline follow-back. In Measuring alcohol consumption (pp. 41-72). Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-4612- 0357-5_3 Sokolovsky, A. W., Gunn, R. L., Micalizzi, L., White, H. R., & Jackson, K. M. (2020). Alcohol and marijuana co-use: Consequences, subjective intoxication, and the operationalization of simultaneous use. Drug and alcohol dependence, 212, 107986. Subbaraman M. S. (2016). Substitution and Complementarity of Alcohol and Cannabis: A Review of the Literature. Substance use & misuse, 51(11), 1399-414. https://doi.org/10.3109/10826084.2016.1170145 Statistics Canada. (2021a). Table 13-10-0096-11 Heavy drinking, by age group 94Statistics Canada. (2021b). Alcohol and cannabis use during the pandemic: Canadian Perspecitive Series 6. Stavro, K., Pelletier, J., & Potvin, S. (2013). Widespread and sustained cognitive deficits in alcoholism: a meta-analysis. Addiction biology, 18(2), 203-213. https://doi.org/10.1111/j.1369-1600.2011.00418.x Subbaraman, M. S., & Kerr, W. C. (2015). Simultaneous versus concurrent use of alcohol and cannabis in the National Alcohol Survey. Alcoholism: Clinical and Experimental Research, 39(5), 872-879. Simons, J. S., Dvorak, R. D., Merrill, J. E., & Read, J. P. (2012). Dimensions and severity of marijuana consequences: Development and validation of the Marijuana Consequences Questionnaire (MACQ). Addictive Behaviors, 37(5), 613-621. Taylor, B., Irving, H. M., Kanteres, F., Room, R., Borges, G., Cherpitel, C., ... & Rehm, J. (2010). The more you drink, the harder you fall: a systematic review and meta-analysis of how acute alcohol consumption and injury or collision risk increase together. Drug and alcohol dependence, 110(1-2), 108-116. Terry-McElrath, Y. M., O’Malley, P. M., & Johnston, L. D. (2013). Simultaneous alcohol and marijuana use among US high school seniors from 1976 to 2011: Trends, reasons, and situations. Drug and Alcohol Dependence, 133(1), 71-79. Treloar, H. R., Morris, D. H., Pedersen, S. L., & McCarthy, D. M. (2012). Direct and indirect effects of impulsivity traits on drinking and driving in young adults. Journal of studies on alcohol and drugs, 73(5), 794-803. 95Waddell, J. T., Gunn, R. L., Corbin, W. R., Borsari, B., & Metrik, J. (2021). Drinking less on cannabis use days: The moderating role of UPPS-P impulsive personality traits. Psychology of Addictive Behaviors. Wagner, M. T., Mithoefer, M. C., Mithoefer, A. T., MacAulay, R. K., Jerome, L., Yazar-Klosinski, B., & Doblin, R. (2017). Therapeutic effect of increased openness: Investigating mechanism of action in MDMA-assisted psychotherapy. Journal of Psychopharmacology, 31(8), 967-974. White, A., & Hingson, R. (2014). The burden of alcohol use: excessive alcohol consumption and related consequences among college students. Alcohol Research: Current Reviews, 35(2), 201– 218. White, H. R., Kilmer, J. R., Fossos‐Wong, N., Hayes, K., Sokolovsky, A. W., & Jackson, K. M. (2019). Simultaneous alcohol and marijuana use among college students: Patterns, correlates, norms, and consequences. Alcoholism: clinical and experimental research, 43(7), 1545-1555. Yaden, D. B., & Griffiths, R. R. (2020). The subjective effects of psychedelics are necessary for their enduring therapeutic effects. ACS Pharmacology & Translational Science, 4(2), 568-572. Quinn, P. D., & Fromme, K. (2011). Subjective response to alcohol challenge: a quantitative review. Alcoholism: Clinical and Experimental Research, 35(10), 1759-1770. Zeiger, J. S., Haberstick, B. C., Corley, R. P., Ehringer, M. A., Crowley, T. J., Hewitt, J. K., ... & Rhee, S. H. (2010). Subjective effects to marijuana associated with marijuana use in community and clinical subjects. Drug and alcohol dependence, 109(1-3), 161-166. 96Appendices Appendix A Baseline Survey. 1. What is your gender? ● Male ● Female ● Transgender ● Non-Binary ● Other: Please specify____________ ● Prefer Not to Say 2. Please select your age: 2.1. Prefer not to say 3. Please select the orientation that best applies to you: ● Straight/Heterosexual ● Gay/Lesbian/Homosexual ● Bisexual ● Asexual ● Pansexual ● Other: Please specify ______________ Free text entry (numeric only) 97● Prefer not to say 4. Please select the ethnicity that best applies to you (select all that apply): ● Asian ● Black / African ● Caucasian ● Hispanic / Latino ● Middle Eastern / North African ● Indigenous / First Nations ● Metis ● Pacific Islander ● Other: Please specify ______________ ● Prefer not to say 5. Do you currently live: o On campus alone o On campus with roommates/family o Off campus alone o Off Campus with roommates/family 6. Please select your height (in cm) Free text entry (numeric only) 98• Prefer not to say 7. Please select your weight (in kg): • Prefer not to say 8. On average, how many days do you drink alcohol in a month? • • Less than monthly • Never • Prefer not to say 9. On average, how many days do you consume, vape, or smoke cannabis in a month? • • Less than monthly • Never • Prefer not to say 10. On average, how many days do you co-use cannabis and alcohol (use both substances so that the effects overlap) in a month? 11. 12. Less than monthly Free text entry (numeric only) Free text entry (numeric only) Free text entry (numeric only) Free text entry (numeric only) 9913. Never 14. Prefer not to say For participants that report any co-use in question 10: a) Please indicate the extent to which you agree with the following statements (slider scale 1 = Very strongly disagree, 5 = very strongly agree shown following every statement) a. “I use cannabis to purposely reduce the amount of alcohol I use” b. “The more cannabis I use the more alcohol I drink” c. “When I use cannabis I crave alcohol” d. “I use cannabis together with alcohol because I like the feeling better than the alcohol on its own” 100Appendix B EMA surveys. Daily EMAs. how many drinks have you had today 1. Which best describes you right now: a) I have had at least one drink today b) I have not had any drinks, but I plan to later c) I have not had any drinks today, and I don’t plan to for the rest of the evening d) prefer not to say If the participant selects either (a) or (b) to the question #1 above, the following is presented 1.1. Please estimate to your best ability, what time you will be having your last drink of the night a) Drop down with 30 minute intervals between each time (e.g., 12, 12:30, 1, 1:30 etc.) b) Prefer not to say End-of-evening EMAs This EMA is set to be triggered 30 minutes before the participant estimates they will have their last drink in question 1.1 above. 1. Which best describes you right now: a) I have had at least one drink today b) I have not had any drinks, but I plan to later 101c) I have not had any drinks today, and I don’t plan to for the rest of the evening d) prefer not to say If the participant selects (a) to question #1 above, the following is presented 1.1. how many drinks have you had today a) Free numeric response b) Prefer not to say 1.2.were all these drinks consumed as one drinking event? In other words, no more than 3 hours separating two drinks” a) Yes b) No c) Prefer not to say If the participant selects either (b) to the question #1.2 above, the following is presented 1.3.1 How many drinks were consumed in the most recent of these drinking events? a) Free numeric response b) Prefer not to say 1.3. Have you used cannabis within two hours of this drinking event? a) Yes b) No c) Prefer not to say If the participant selects either (a) to the question #1.3 above, the following is presented 1.3.1 Approximately how long ago did you use cannabis? 102a) Drop down menu with time options (e.g., 15 minutes ago, 30 minutes ago, 1 hour ago, etc.) b) Prefer not to say 1.3.2 Please select which option best describes your cannabis use with respect to your alcohol use If the participant selects (a) to question #1 above, the following is presented • B-BAES items 1-6 (see appendix C) Morning-After EMA This EMA is set to be triggered at 10am the morning following a drinking event. 1. Which best describes you: “I used cannabis today more than two hours before my first drink.” “I used cannabis today less than two hours before my first drink” “I used cannabis after last drink” Two hours from drink First drink Last drink “I used cannabis today in between my first and last drink” 103a) I did not drink yesterday b) I drank yesterday c) prefer not to say If the participant selects (b) to question #1 above, the following is presented 1.4. how many drinks did you have yesterday in total. c) Free numeric response d) Prefer not to say 1.5.were all these drinks consumed as one drinking event? In other words, no more than 3 hours separating two drinks” d) Yes e) No f) Prefer not to say If the participant selects either (b) to the question #1.2 above, the following is presented 1.3.1 How many drinks were consumed in the most recent of these drinking events? c) Free numeric response d) Prefer not to say 1.6. Did you used cannabis within two hours of this drinking event? a) Yes b) No c) Prefer not to say 1041.7.Please select all the options that describe your use of cannabis with respect to your use of alcohol 1.8.List of co-use consequences (see appendix D) “I used cannabis today more than two hours before my first drink.” “I used cannabis today less than two hours before my first drink” “I used cannabis after last drink” Two hours from drink First drink Last drink 105Appendix C Not at all Extremely 0 1 2 3 4 5 6 7 8 9 10 106Appendix D Co-use related consequences (taken from Mallett et al., 2019). Please indicate whether or not you have experienced any of the following in your previous drinking event. Yes No Prefer not to say 1. I said or did embarrassing things 2. I passed out 3. I took foolish risks 4. I drank more alcohol than I originally had planned 5. I became more high/intoxicated than I originally had planned. 6. I got into a physical fight. 7. I became very rude, obnoxious, or insulting 8. I damaged property or did something disruptive such as setting off a false fire alarm or other things like that. 9. I did not eat properly. 10. I woke up in an unexpected place 11. I neglected to protect myself, or my partner, from a sexually transmitted disease (STD) or an unwanted pregnancy. 12. I neglected my obligations to family, work, or school 10713. I found it difficult to limit how much of the substance(s) I took. 14. I experienced a sexual situation I regretted. 15. I said harsh or cruel things to someone. 16. I said things that I later regretted. 17. I did not sleep properly. 18. . My physical appearance was harmed. 19. I was pressured or forced to have sex with someone. 20. I injured someone else. 21. I had a blackout (i.e., could not remember hours at a time). 22. I felt depressed or sad. 23. I made inappropriate sexual advances toward someone. 24. . I was not able to do my homework or study for a test. 25. I went to work or school high or intoxicated. 26. My friends or relatives avoided me. 27. I drove a car while high or intoxicated. 28. I rode in a car with a driver who was high or intoxicated 29. I felt dizzy 30. I felt anxious. 31. I felt nauseous. 32. I had a headache. 33. I vomited. 108Appendix E Model Building Process Step Level Variable type Effect type Variable 1 Within Primary Fixed 1. Alcohol Use 2. Co-use 2 Within Primary Random 1. Alcohol Use 2. Co-use 3 Within Primary Interactions Fixed 1. Significant variables from previous steps 4 Within Primary Interactions Random 1. Significant variables from previous steps 5 Between Primary Covariates Fixed 1. Co-use 2. Alcohol Use 6 Within Covariates Fixed 1. Conformity motivation 2. Enhancement motivation 3. Expansion motivation 4. Social motivation 5. Coping motivation 6. Alone or with others 7 Within Covariate interactions Fixed 1. Significant variables from previous steps 8 Within Covariate interactions Random 1. Significant variables from previous steps 9 Between Covariates Fixed 1. Gender dummy coded (Male baseline) a. Female = 1 b. Transgender/non-binary = 1 2. Conformity motivation 3. Enhancement motivation 4. Social motivation 5. Coping motivation 10 Cross-level Cross level interactions Fixed 1. Significant variables from previous steps 11 Cross-level Cross level interactions Random 1. Significant variables from previous steps Note. Variables were added individually within each step and model fit was assessed via the Akaike Information Criteria (AIC). Variables were then entered in order of best fitting model within the step. Non-significant variables were dropped from the model unless shown to improve model fit and be significant at p = .1. For mediation models, the between-person effects of primary predictors and mediators were forced into the model in step 1. 109 Appendix F Models constructed within the models assessing the mediating effect of sedation onto the relationship between co-use and harms. Outcome Variable β F-value Harms (Intercept) -0.32 1.73 Co-use 0.04 0.10 Typical co-use 0.80 5.06 Typical number of drinks -0.23 3.61 Typical Sedation 0.01 0.11 Number of drinks*** 0.17 143.78 Sedation* 0.05 6.49 Number of drinks (Intercept)*** 0.68 43.20 Typical co-use -0.31 3.26 Co-use*** 0.57 49.65 Typical number of drinks *** 0.29 36.49 Expansion motivation (within)* 0.07 7.44 Sedation (Intercept)* 1.29 7.05 Typical co-use -0.25 0.18 Co-use* 1.07 9.09 Coping motivation (within)* 0.60 9.26 Typical number of drinks (Intercept)*** 1.15 31.93 Typical co-use 0.23 0.31 Typical sedation (Intercept)*** 2.81 110.543 Typical co-use 1.27 5.51 110 Models constructed within the models assessing the mediating effect of sedation onto the relationship between co-use and harms. Outcome Variable β F-value Harms (Intercept) 0.28 0.90 Co-use 0.08 0.49 Typical Co-use* 0.92 6.17 Typical number of drinks -0.20 2.53 Typical stimulation** -0.13 7.50 Number of drinks*** 0.17 139.31 Stimulation 0.02 1.14 Number of drinks (Intercept)*** 0.68 43.20 Typical co-use -0.31 3.26 Co-use*** 0.57 49.65 Typical number of drinks*** 0.29 36.49 Expansion motivation (within)** 0.07 7.44 Stimulation (Intercept)*** 4.34 65.36 Typical co-use 0.21 0.10 Co-use 0.11 0.08 Coping motivation (within) -0.05 0.05 Typical number of drinks (Intercept)*** 1.15 31.93 Typical co-use 0.23 0.31 Typical stimulation (Intercept)*** 4.22 252.22 Typical co-use 0.43 0.63
Thesis/Dissertation
eng
Psychology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Walsh, Zach
Cannabis-alcohol co-use protects from drinking regret without increasing harms
Text