Health and Social Development, Faculty of (Okanagan)
Nursing, School of (Okanagan)
DSpace
UBCO
Halonen, Alex
Master of Science in Nursing - MSN
University of British Columbia
The Coronavirus Disease of 2019 (COVID-19) pandemic highlighted challenges of implementing mass immunization programs for a novel vaccine. COVID-19 vaccine uptake in British Columbia (B.C.) was significantly lower in populations of children under 12 when compared with adults. Vaccine hesitancy, an emerging public health threat, was identified as a potential key contributor to the challenges faced in COVID-19 vaccine uptake. The World Health Organization (WHO) Vaccine Hesitancy Scale (VHS) was modified for pediatric COVID-19 vaccines. An online cross-sectional survey was conducted in B.C. from October to December 2021 to understand COVID-19 vaccine intentions and perceptions of parents of children under the age of 12. We used logistic regression models to assess factors associated with parental vaccine hesitancy and to explore the relationship between a parent’s intention to vaccinate their child with a COVID-19 vaccine and vaccine hesitancy. Findings suggested about one-half of parents included in the study were vaccine hesitant for pediatric COVID-19 vaccines. The number of children under 12 in the household, healthcare provider (HCP) vaccine recommendation, parent COVID-19 vaccine status, and child influenza immunization history were identified as significant predictors of pediatric COVID-19 vaccine hesitancy. A statistically significant relationship between parents’ intentions to vaccinate their child and vaccine hesitancy was found, indicating application of the scale could be of clinical significance. The findings from this study identified target parent groups with increased vaccine hesitancy for the pediatric COVID-19 vaccine. The factors influencing vaccine decision-making identified in this study could provide avenues for ongoing COVID-19 vaccine promotion among parents or for a future public health crisis requiring an emergent introduction of a novel pediatric vaccine. The findings from this study also support the use of the VHS as a clinical tool to screen for vaccine hesitancy, allowing for a more strategic implementation of vaccine- promotion resources.
UNDERSTANDING COVID-19 VACCINE PERCEPTIONS DURING PARENTHOOD IN BRITISH COLUMBIA by Alex Halonen B.S.N., University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NURSING in THE COLLEGE OF GRADUATE STUDIES (Nursing) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) March 2024 © Alex Halonen, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: UNDERSTANDING COVID-19 VACCINE PERCEPTIONS DURING PARENTHOOD IN BRITISH COLUMBIA submitted by Alex Halonen in partial fulfillment of the requirements of the degree of Master of Science in Nursing. Dr. Marie Tarrant, Faculty of Health and Social Development Supervisor Dr. Elizabeth Keys, School of Nursing Supervisory Committee Member Rishma Chooniedass, School of Nursing Supervisory Committee Member Dr. Julie Bettinger, Faculty of Medicine University Examiner iii Abstract The Coronavirus Disease of 2019 (COVID-19) pandemic highlighted challenges of implementing mass immunization programs for a novel vaccine. COVID-19 vaccine uptake in British Columbia (B.C.) was significantly lower in populations of children under 12 when compared with adults. Vaccine hesitancy, an emerging public health threat, was identified as a potential key contributor to the challenges faced in COVID-19 vaccine uptake. The World Health Organization (WHO) Vaccine Hesitancy Scale (VHS) was modified for pediatric COVID-19 vaccines. An online cross-sectional survey was conducted in B.C. from October to December 2021 to understand COVID-19 vaccine intentions and perceptions of parents of children under the age of 12. We used logistic regression models to assess factors associated with parental vaccine hesitancy and to explore the relationship between a parent’s intention to vaccinate their child with a COVID-19 vaccine and vaccine hesitancy. Findings suggested about one-half of parents included in the study were vaccine hesitant for pediatric COVID-19 vaccines. The number of children under 12 in the household, healthcare provider (HCP) vaccine recommendation, parent COVID-19 vaccine status, and child influenza immunization history were identified as significant predictors of pediatric COVID-19 vaccine hesitancy. A statistically significant relationship between parents’ intentions to vaccinate their child and vaccine hesitancy was found, indicating application of the scale could be of clinical significance. The findings from this study identified target parent groups with increased vaccine hesitancy for the pediatric COVID-19 vaccine. The factors influencing vaccine decision-making identified in this study could provide avenues for ongoing COVID-19 vaccine promotion among parents or for a future public health crisis requiring an emergent introduction of a novel pediatric vaccine. The findings from this study also support the use of the VHS as a clinical tool to screen for vaccine hesitancy, allowing for a more strategic implementation of vaccine-promotion resources. iv Lay Summary Coronavirus Disease of 2019 (COVID-19) vaccine rates among children in British Columbia (B.C.) were significantly lower than adults. Vaccine hesitancy may have been contributing to decreased COVID-19 vaccine uptake in child populations. This study aimed to understand the presence and contributing factors of COVID-19 vaccine hesitancy among a sample of parents of children under 12 in B.C. by modifying a scale measuring vaccine hesitancy to be specific for COVID-19 vaccines for children. The results suggested that about one-half of parents included in this study were vaccine hesitant towards COVID-19 vaccines. The modified tool to measure vaccine hesitancy demonstrated a strong relationship with intentions to vaccinate a child for COVID-19, indicating that adopting this tool in a clinical setting to identify vaccine hesitancy could be beneficial to provide individualized education on vaccinations. v Preface This thesis is original, unpublished work by the author, Alex Halonen, with guidance from my supervisor Dr. Marie Tarrant and committee members Dr. Elizabeth Keys and Rishma Chooniedass. The survey data used in this study was previously collected as part of a larger study lead by Dr. Marie Tarrant for the Maternal and Child Health Lab at the University of British Columbia Okanagan (UBCO). The larger study was partially funded by Genome British Columbia, the Michael Smith Foundation for Health Research, and the BCCDC Foundation. The data was not previously analyzed and the objectives of this study aligned with the overall objectives of the larger study. For those reasons, the study fell within the original ethics approval from the Behavioural Research Ethics Board of the UBCO. Approval number H21-01543. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiii Dedication ................................................................................................................................... xiv Chapter 1: Introduction ................................................................................................................1 1.1 Background ................................................................................................................... 1 1.1.1 Provincial COVID-19 Management ....................................................................... 2 1.1.2 Pediatric COVID-19 Morbidity and Mortality ....................................................... 3 1.1.3 Pediatric COVID-19 Vaccination ........................................................................... 3 1.1.4 Vaccine Hesitancy .................................................................................................. 5 1.1.4.1 Vaccine Hesitancy Matrix................................................................................... 6 1.1.4.2 Quantifying Vaccine Hesitancy .......................................................................... 8 1.1.4.3 Pediatric COVID-19 Vaccine Hesitancy .......................................................... 10 1.2 Research Purpose and Objectives ............................................................................... 11 Chapter 2: Systematic Literature Review ................................................................................12 2.1 Methods....................................................................................................................... 12 2.1.1 Eligibility Criteria ................................................................................................. 12 vii 2.1.2 Information Sources .............................................................................................. 13 2.1.3 Search Strategy ..................................................................................................... 13 2.1.4 Selection Process .................................................................................................. 14 2.1.5 Data Items ............................................................................................................. 14 2.1.6 Risk of Bias Assessment ....................................................................................... 17 2.2 Results ......................................................................................................................... 17 2.2.1 Study Selection ..................................................................................................... 20 2.2.2 Study Characteristics ............................................................................................ 21 2.2.2.1 Application of Vaccine Hesitancy Scale........................................................... 26 2.2.3 Quality Assessment of Studies.............................................................................. 27 2.2.4 Main Findings ....................................................................................................... 30 2.2.4.1 Contextual Influences ....................................................................................... 30 2.2.4.2 Individual Influences ........................................................................................ 31 2.2.4.3 Vaccine-specific Influences .............................................................................. 32 2.3 Discussion ................................................................................................................... 33 2.3.1 Limitations ............................................................................................................ 36 2.3.2 Applicability to Current Study .............................................................................. 36 Chapter 3: Methodology..............................................................................................................38 3.1 Study Design ............................................................................................................... 38 3.2 Study Participants ....................................................................................................... 39 3.3 Participant Recruitment .............................................................................................. 39 3.4 Sample Size Calculation ............................................................................................. 40 3.5 Data Collection ........................................................................................................... 40 viii 3.6 Survey Procedure ........................................................................................................ 42 3.7 Study Measures ........................................................................................................... 42 3.7.1 Outcome Variable - Vaccine Hesitancy ................................................................ 42 3.7.2 Covariates – Contextual Factors ........................................................................... 44 3.7.3 Covariates – Individual Factors ............................................................................ 45 3.7.4 Covariates – Vaccine-Specific Factors ................................................................. 46 3.7.5 Additional Variable of Interest ............................................................................. 46 3.8 Data Analysis Plan ...................................................................................................... 47 3.8.1 Missing Data ......................................................................................................... 47 3.8.2 Statistical Analysis ................................................................................................ 48 3.8.2.1 Prevalence of Vaccine Hesitancy...................................................................... 48 3.8.2.2 Predictors of Vaccine Hesitancy ....................................................................... 49 3.8.2.3 Vaccine Hesitancy Scale and Vaccine Intentions ............................................. 50 3.9 Ethical Considerations ................................................................................................ 51 3.9.1 Risk Mitigation .................................................................................................... 52 Chapter 4: Results........................................................................................................................53 4.1 Participant Inclusion and Exclusion............................................................................ 53 4.2 Participant Characteristics .......................................................................................... 54 4.2.1 Reasons for Vaccine Intention .............................................................................. 56 4.3 Prevalence of Vaccine Hesitancy................................................................................ 59 4.4 Predictors of Vaccine Hesitancy ................................................................................. 65 4.5 Vaccine Hesitancy and Vaccine Intentions................................................................. 71 Chapter 5: Conclusion .................................................................................................................73 ix 5.1 Discussion ................................................................................................................... 73 5.1.1 Clinical Significance ............................................................................................. 79 5.2 Strengths, Limitations and Future Recommendations ................................................ 81 5.3 Personal Reflection ..................................................................................................... 83 5.4 Concluding Statement ................................................................................................. 85 Bibliography .................................................................................................................................86 x List of Tables Table 1 Keyword Search Strategy ................................................................................................ 14 Table 2 Search Strategy by Database............................................................................................ 15 Table 3 Reasons for Full-Text Exclusion ..................................................................................... 21 Table 4 Critical Appraisal Results ................................................................................................ 29 Table 5 Demographic Characteristics (N = 993) .......................................................................... 55 Table 6 Health and Vaccination Characteristics (N=993) ............................................................ 56 Table 7 Parents of Children Under 12 Total Mean VHS Scores .................................................. 63 Table 8 Unadjusted Associations Between Matrix Factors and Vaccine Hesitancy (N=993) ..... 69 Table 9 Adjusted Associations Between Matrix Factors and Vaccine Hesitancy (N=993) ......... 70 Table 10 Association Between Vaccine Hesitancy and Intentions with Covariates (N=993) ...... 72 xi List of Figures Figure 1 PRISMA Flow Diagram ................................................................................................. 19 Figure 2 Average Case Count During Surveying Period .............................................................. 41 Figure 3 Participant Flow Diagram............................................................................................... 54 Figure 4 Reasons for Willingness to Accept a COVID-19 Vaccine for a Child (N=993)............ 57 Figure 5 Reasons for Unwillingness to Accept a COVID-19 Vaccine for a Child (N=993) ....... 58 Figure 6 Response for Individual VHS Items (N=993) ................................................................ 60 Figure 7 Parents of Children Under 12 Mean Score Per VHS Item (N=993) .............................. 64 xii List of Abbreviations B.C. British Columbia BCCDC BC Centre for Disease Control CI Confidence Interval CINAHL Cumulative Index to Nursing and Allied Health Literature COVID-19 Coronavirus Disease of 2019 JBI Joanna Briggs Institute HCP Healthcare Provider HPV Human Papillomavirus IP Internet Protocol OR Odds Ratio PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analysis SAGE Strategic Advisory Group of Experts UBCO University of British Columbia Okanagan VIF Variance Inflation Factor VHS Vaccine Hesitancy Scale WHO World Health Organization xiii Acknowledgements I would like to begin by thanking my supervisor, Dr. Marie Tarrant, for her guidance during my time as a research assistant and throughout the duration of my graduate degree. Your knowledge and passion for maternal and child vaccine promotion has exposed me to new areas of nursing and health promotion. Your patience and flexibility to support me maintaining a clinical position while completing the thesis stream of this master’s program has greatly contributed to my success balancing the two demands. Dr. Elizabeth Keys and Rishma Chooniedass, thank you for challenging me, engaging in insightful conversation, and for supporting me throughout my thesis writing. I am grateful for your time you committed to helping me succeed in this process. I would also like to thank Dr. Heidi Sze Lok Fan, Stephanie Masina, and Karyann Dorn for their work as part of the larger study with the Maternal and Child Health Lab. In addition to her work on the larger study, I would also like to thank Dr. Heidi Sze Lok Fan for providing me support in a moment’s notice while I learned to navigate a new statistical software program. Finally, I would like to thank my family, friends, and colleagues who have encouraged me over the last two years. Thank you for your understanding as I put many aspects of my social life on pause to complete my studies. xiv Dedication To future graduate nursing students, continue to challenge the idea that completing a graduate education and maintaining a role in clinical practice must be separate entities. 1 Chapter 1: Introduction In this chapter I establish a background on the coronavirus disease of 2019 (COVID-19) pandemic and describes the concept of vaccine hesitancy. I explain the context of the COVID-19 pandemic in a pediatric population and in British Columbia (B.C.). The purpose of the study and research objectives are then defined. 1.1 Background The World Health Organization (WHO) declared a pandemic in March 2020 when cases of COVID-19 rapidly spread outside China in more than 100 countries (WHO, 2020). COVID-19 is caused by the virus SARS-CoV-2 and is transmitted via droplets and aerosolization (Government of Canada, 2023a). The severity of illness varies significantly between individuals who contract COVID-19. Individuals who test positive for COVID-19 range from requiring acute hospitalization to being asymptomatic virus carriers (Government of Canada, 2023a). This range in severity of illness is also observed within the pediatric population, with most children who test positive for COVID-19 experiencing mild or asymptomatic illness (Government of Canada, 2023c). As of March 2024, the global burden of COVID-19 has included over 700 million confirmed cases and seven million deaths (WHO, 2024). In addition to the health consequences of COVID-19, Canadians have experienced economic and social implications of public health restrictions from the pandemic (Statistics Canada, 2022a). The development of effective vaccines was identified as a foundational pandemic control strategy that could lessen the health, economic, and social burdens of the COVID-19 pandemic (WHO, 2021). 2 1.1.1 Provincial COVID-19 Management B.C. is the most western province in Canada with a population of 5,000,879 people (Statistics Canada, 2022b). The Ministry of Health oversees five regional health authorities that work together to govern and implement health programs across the province, such as pandemic management (Government of British Columbia, n.d.). The province’s control of the emerging pandemic was strong (De Faye et al., 2022). As of March 2024, there have been 418,182 confirmed cases of COVID-19 in B.C., and 68,585 (16.4%) of the cases have occurred in people under 19 years of age (BC Centre for Disease Control [BCCDC], 2024). This value only reflects positive cases reported to the government, meaning that the true number of cases is unknown and could be much higher given that self-testing kits were readily available across the province. Information about public health restrictions, government mandates, and vaccine updates was primarily disseminated by the Provincial Health Officer during daily or weekly press conferences (De Faye et al., 2022). The initial response from people in B.C. included trust and cooperation, which minimized case numbers and hospital overcapacity (De Faye et al., 2022; Thiara et al., 2020). As the Government of Canada approved COVID-19 vaccines for adults in December 2020, press conferences and public health messaging from the Public Health Officer and provincial government focused mainly on vaccine promotion (De Faye et al., 2022). To successfully implement the province’s most extensive immunization program, the government collaborated with the private sector and relied on the strengths of pre-existing health agencies to develop effective vaccine messaging, track immunization progress, and administer vaccines (De Faye et al., 2022). In comparison to other Canadian provinces, B.C. had higher adult COVID-19 vaccination rates (Government of Canada, 2024). 3 1.1.2 Pediatric COVID-19 Morbidity and Mortality While severity of COVID-19 infections in children is commonly mild, some children experienced significant illness or died due to COVID-19 infection (Shekerdemian et al., 2020). Since March 2024, there has been a cumulative total of three deaths in B.C. in people under the age of 10 with an underlying cause of death identified as COVID-19 (BCCDC, 2024). Since the reporting period, the provincial cumulative number of people under age 19 requiring hospitalization from COVID-19 was 1244, with 177 people requiring admission to critical care (BCCDC, 2024). While most children experienced mild cold-like symptoms from COVID-19, the seriousness of pediatric COVID-19 infections is exemplified by cases of children experiencing single or multiple system organ failure or requiring critical care interventions, including mechanical ventilation via endotracheal intubation or tracheostomies as a result of respiratory compromise (Shekerdemian et al., 2020). In addition to the effects on physical health, the impact of the COVID-19 pandemic on pediatric mental health in B.C. should also be considered when discussing morbidity. Public health restrictions and mandates implemented to maintain public safety (e.g., school closures, social distancing) inadvertently impacted children’s mental health, especially socioeconomic disadvantaged families, through increased experiences of isolation, separation, or grief and decreased access to social services or programs (Waddell et al., 2020). 1.1.3 Pediatric COVID-19 Vaccination Pediatric COVID-19 vaccination contributes to decreased viral transmission in pediatric, adult, high-risk, and older adult populations (Government of Canada, 2023c; Paul et al., 2021). Health Canada first approved pediatric COVID-19 vaccines for children aged 5 to 11 years in November 2021 and they were available in B.C. the following week (Government of British 4 Columbia, 2021; Health Canada, 2021). Health Canada then approved COVID-19 vaccines for children aged six months to 5 years of age in July 2022, which were available for British Columbians in early August 2022 (Government of British Columbia, 2022; Health Canada, 2022). Despite the success of adult vaccinations, pediatric COVID-19 vaccination coverage remained significantly lower than adult coverage across the province and country (Government of Canada, 2024). As of March 2024, 44.7% of children aged 5 to 11 years and 9.3% of children aged 0 to 4 years in Canada have received at least one dose of a COVID-19 vaccine (Government of Canada, 2024). In B.C., the percentage of children vaccinated with one dose of a COVID-19 vaccine as of March 2024 aged 5 to 11 years is similar to the national value at 45% (Government of Canada, 2024). The percentage of children in B.C. aged 0 to 4 years of age vaccinated with a single COVID-19 vaccine is double the national value at 18.7%, but still suboptimal (Government of Canada, 2024). Similar to the adult series of COVID-19 vaccines, the Government of Canada initially recommended children received a primary series of vaccines that included two to three doses of the COVID-19 vaccine depending on the manufacturer (Government of Canada, 2023c). The BCCDC released their final update on vaccine uptake in July 2023 and only 43% of children aged 5 to 11 years and 16% of children aged 0 to 4 years had received a second dose of a COVID-19 vaccine in comparison to 91% of British Columbian older than 18 years of age who had received a second dose of a COVID-19 vaccine (BCCDC, 2023b). It is important to consider that proof of COVID-19 vaccinations for adults in the form of vaccine passports was a requirement of B.C. and some other provinces to access many recreational activities at the peak of the COVID-19 pandemic, potentially contributing to higher rates of vaccination in adult populations when compared to pediatric populations (De Faye et al., 2022). Vaccine recommendations continue to evolve into early 2024 and pediatric vaccine 5 uptake continues to remain suboptimal when compared with adult uptake at both provincial and national levels (Government of Canada, 2024) 1.1.4 Vaccine Hesitancy Vaccine decision-making involves a complex process of accepting or refusing a vaccine based on individual, contextual, and vaccine-specific factors (Larson et al., 2014). Multiple definitions and interpretations of vaccine hesitancy exist across public health research (Dube et al., 2013). The WHO Strategic Advisory Group of Experts (SAGE) Working Group on Vaccine Hesitancy defined vaccine hesitancy as “the refusal of vaccines despite the availability of vaccination services” (WHO, 2014, p. 7). This definition is well accepted globally and underpins the Vaccine Hesitancy Matrix and Vaccine Hesitancy Scale (VHS), the guiding framework and the primary metric for analysis in this thesis (Larson et al., 2015; WHO, 2014). However, this relatively simple definition only focusses on the act of vaccine refusal, minimizing the complexity of vaccine hesitancy. Vaccine hesitancy is a highly complex behavioural phenomenon that may be better described as a continuum, where individuals can range from accepting all vaccines to refusing all vaccines (MacDonald & The SAGE Working Group on Vaccine Hesitancy, 2015). Vaccine hesitancy is contextual and fluid, where individuals may refuse one vaccine but accept others or delay vaccination temporarily (WHO, 2014). The fluidity of vaccine hesitancy also lends itself to encountering its alternative, vaccine confidence, when exploring various models or metrics for vaccine hesitancy (WHO, 2014). When assessing underlying reasons for vaccine hesitancy, a factor may contribute to hesitancy for one individual while the same factor contributes to confidence for another (Larson et al., 2014). Vaccine hesitancy is often associated with parental refusal of childhood vaccines or delays in routine vaccine schedules (Dube et al., 2014). In a pediatric context, parents are the decision-makers in 6 the vaccine consent process, making them the target group for vaccine promotion to decrease vaccine hesitancy specific to pediatric immunizations (Salmon et al., 2015). Parents can be considered vaccine hesitant if they refuse to vaccinate their child with one or more vaccines, disrupt their child’s immunization schedule, or continue to have concerns after their child receives a vaccine (Salmon et al., 2015). Parental vaccine hesitancy not only affects the unvaccinated child, but it can also have implications for entire populations if vaccine rates decline significantly enough to cause a re-emergence of vaccine-preventable disease (Olson et al., 2020; Salmon et al., 2015). Experts have been raising concerns about decreases in routine vaccine rates and increasing cases of vaccine-preventable disease in countries that have typically had high vaccine coverage (Dube et al., 2016; Salmon et al., 2015). These concerns have reinforced that vaccine hesitancy must be both a Canadian and a global public health priority (Dube et al., 2016; WHO, 2019b). 1.1.4.1 Vaccine Hesitancy Matrix Causes of vaccine hesitancy are highly complex and multifactorial (Dube et al., 2016; Dube et al., 2013; Larson et al., 2014). The Vaccine Hesitancy Matrix, developed by the SAGE Working Group on Vaccine Hesitancy, presents a comprehensive model of vaccine hesitancy that will be used as a guiding framework for this thesis (WHO, 2014). The overall premise of the Vaccine Hesitancy Matrix is that influential factors in vaccine decision-making are context-specific, multifactorial, and non-directional (MacDonald & The SAGE Working Group on Vaccine Hesitancy, 2015; WHO, 2014). The non-directional nature of the Vaccine Hesitancy Matrix is exemplified with the factor of higher education, where in some settings, higher education is a protective factor against vaccine delay, whereas, in others, it is predictive of vaccine hesitancy (Larson et al., 2014). The model aims to support researchers and governments 7 in efficiently identifying determinants of vaccine hesitancy specific to their location (WHO, 2015). The development of the model and decisions on what influential factors were included in the model were derived from a combination of vaccine experts, research, literature reviews, and global leaders in immunization work (WHO, 2014). This model divides factors contributing to vaccine hesitancy into contextual, individual, and vaccine/vaccination-specific categories. Contextual influences include any “influences arising due to historical, socio-cultural, environmental, health system/institutional, economic or political factors” that influence an individual to delay or refuse a vaccine (WHO, 2015, p. 3). This includes social media, community or religious leaders, pharmaceutical companies, socioeconomic or cultural indicators, and geographical barriers influencing vaccine hesitancy (MacDonald & The SAGE Working Group on Vaccine Hesitancy, 2015; WHO, 2014; WHO, 2015). Individual influences include those “arising from the personal perception of the vaccine or influences of the social/peer environment” (WHO, 2015, p. 4). This includes factors influencing vaccine hesitancy, such as vaccination history, general health literacy, health beliefs, social norms, health system trust, and perceived risks and benefits (MacDonald & The SAGE Working Group on Vaccine Hesitancy, 2015; WHO, 2014; WHO, 2015). Vaccine-specific influences include issues “directly related to vaccines or vaccination” that influence an individual to refuse or delay vaccination (WHO, 2015, p. 5). These include scientific evidence, the novelty of a vaccine, the design of an immunization program, healthcare provide (HCP) recommendations, route of administration or schedule of delivery, and cost as influential factors for vaccine hesitancy (MacDonald & The SAGE Working Group on Vaccine Hesitancy, 2015; WHO, 2014; WHO, 2015). Post-development, a systematic review of over 1000 articles and a qualitative study with 13 global immunization managers did not identify any additional categories or factors to be included in the model (Dube 8 et al., 2014; Larson et al., 2014). By identifying the determinants of vaccine hesitancy specific to location, appropriate targeted strategies can be developed to increase vaccine uptake. 1.1.4.2 Quantifying Vaccine Hesitancy Measuring vaccine hesitancy allows for a more objective understanding of the degree of the problem and more strategic deployment of targeted vaccine promotion strategies (Gilkey et al., 2014; WHO, 2014). By quantifying vaccine hesitancy, researchers and health professionals can compare the prevalence and risk factors across various groups. The quantification of vaccine hesitancy also enables researchers and health professionals to track changes in trends or rates of hesitancy to evaluate the effectiveness of vaccine promotion initiatives (Gilkey et al., 2014; Larson et al., 2015). Multiple tools and scales have been developed and validated to quantify vaccine hesitancy (Larson et al., 2015; Olson et al., 2020; Shapiro et al., 2018). However, the use of measurement tools varies internationally, making comparing vaccine hesitancy between different settings an ambiguous task (Helmkamp et al., 2021; Larson et al., 2015). Following the SAGE Working Group on Vaccine Hesitancy development of a globally accepted definition of vaccine hesitancy and the Vaccine Hesitancy Matrix, immunization leaders called for a standardized global tool for measuring vaccine hesitancy (Larson et al., 2015; MacDonald & The SAGE Working Group on Vaccine Hesitancy, 2015). The VHS provides a standardized assessment tool for consistent global comparison of vaccine hesitancy (Larson et al., 2015). The VHS was developed following significant reviews on existing measurement tools for vaccine hesitancy and consultation with global experts and leaders in immunization programs. The findings from the review were compared to the concepts in the Vaccine Hesitancy Matrix, indicating gaps in existing resources and calling for a more comprehensive assessment tool that aligned with the WHO definitions, scope, and perspective on 9 vaccine hesitancy. The first iteration of the VHS consisted of 10 questions on a 5-point Likert scale from strongly disagree to strongly agree (Larson et al., 2015). Validation of the scale revealed that the last item on the original VHS, “My child/children does or do not need vaccines for diseases that are not common anymore” was unreliable (Shapiro et al., 2018). The validated VHS consists of the nine remaining items. Within the 9-item VHS, two sub-scales were identified, including seven items evaluating a “lack of confidence” and two items evaluating perceived “risks” in vaccines (Shapiro et al., 2018). Since the validation of the original VHS, further modification to the scale occurred to be more specific to individual vaccines (Helmkamp et al., 2021; Kempe et al., 2020). The scale was first modified to an 8-item scale for routine childhood and influenza vaccines (Kempe et al., 2020). During this modification, the Likert scale included in the survey was changed from a 5-point scale to a 4-point scale (Kempe et al., 2020). The decision to move to a 4-point Likert scale was based on the recommendations of Chyung et al. (2017) to remove the neutral point to reduce social desirability bias. Kempe et al. (2020) defined vaccine hesitancy as a mean score of greater than 3 on the 8-item VHS. The VHS was again modified to include language for specific for childhood, influenza, and human papillomavirus (HPV) vaccines (Helmkamp et al., 2021). These revisions resulted in the development of three 9-item validated scales, including the VHS-child for routine childhood immunizations, the VHS-flu for pediatric influenza immunizations, and the VHS-HPV for adolescent HPV immunizations (Helmkamp et al., 2021). The lack of confidence and risk sub-scales remain in these vaccine-specific versions of the VHS, with a reduction in lack of confidence items to six and introduction of a third risk item, addressing a limitation identified during the original validation process (Helmkamp et al., 2021; Shapiro et al., 2018). This modification also included the change from a 5-point to a 4-point Likert scale and a more clearly 10 defined scoring system (Helmkamp et al., 2021). A scale of strongly agree, agree, disagree, and strongly agree with associated scores 1, 2, 4, and 5 respectively, was applied to the three scales (Helmkamp et al., 2021). Vaccine hesitancy was then defined and validated as a mean score of greater than 3. The VHS continues to be modified beyond the 10-item original and 9-item validated versions (Larson et al., 2015; Shapiro et al., 2018). Language continues to change, questions continue to be omitted, and the context of the scale continues to adapt (Akel et al., 2021; Helmkamp et al., 2021; Kempe et al., 2020). During the COVID-19 pandemic when no metrics specific to COVID-19 vaccines existed, the same practices of modifying the VHS to measure vaccine hesitancy specific to the context of COVID-19 vaccines occurred (Temsah et al., 2021). 1.1.4.3 Pediatric COVID-19 Vaccine Hesitancy At the start of the pandemic, vaccine hesitancy was identified as a potential barrier to successful pandemic control strategies for COVID-19, including particular concerns among parental populations (Dror et al., 2020). Explorations of parental vaccine decision-making across North America prior to government approval of pediatric COVID-19 vaccines indicated that vaccine uptake in pediatric populations would be impeded by vaccine hesitancy (Goldman et al., 2020; He et al., 2022). Despite extensive safety guidelines and government bodies’ approval of pediatric COVID-19 vaccines, the low percentage of children in B.C. vaccinated for COVID-19 could indicate hesitancy exists among parents. The prevalence of vaccine hesitancy and the contributing factors to pediatric COVID-19 vaccines specific to B.C. parents have not been measured using the VHS, indicating a need for further exploration of this topic. 11 1.2 Research Purpose and Objectives This thesis aimed to understand individual factors influencing vaccine decision-making and vaccine hesitancy in a group of parents of children under 12 in B.C. The objectives of this thesis included: 1. To examine the prevalence of COVID-19 vaccine hesitancy among parents of children under 12 in B.C. 2. To identify contextual, individual, and vaccine-specific factors that are predictive of COVID-19 vaccine hesitancy among parents of children under 12 in B.C. 3. To examine the relationship between the results from a modified version of the VHS and B.C. parent’s intention to vaccinate their child under 12 with a COVID-19 vaccine. 12 Chapter 2: Systematic Literature Review In this chapter I describe the process of conducting a systematic literature review. Literature was reviewed and critiqued to develop a better understanding of the global body of knowledge on pediatric COVID-19 vaccine hesitancy. The findings from the literature on the application of the VHS for pediatric COVID-19 vaccines and influential factors from the Vaccine Hesitancy Matrix on pediatric COVID-19 vaccine hesitancy are discussed. 2.1 Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 statement was used to guide the systematic review process (Page et al., 2021). This set of guidelines is applied globally as a gold-standard for conducting systematic literature reviews. The objective of this literature review was two-fold. The first objective of this review was to determine how influential factors in parental vaccine decision-making for COVID-19 vaccination in children under the age of 12 are understood in current literature. Having an in-depth understanding of published research on pediatric COVID-19 decision-making, including the underlying factors influencing vaccine hesitancy, allowed for the comparison of relevant findings from this thesis to a global body of knowledge. The second objective of this review was to understand how the VHS had been modified and applied to understand pediatric COVID-19 vaccine hesitancy in the absence of a validated COVID-19 specific tool. Understanding how others had applied the VHS assisted in developing the data analysis plan for this thesis. 2.1.1 Eligibility Criteria Study eligibility for this systematic review included primary studies with the objective of detecting factors influencing parental pediatric vaccine decision-making. Only studies including 13 the application of the VHS to understand the prevalence, predictive factors, or protective factors of parental pediatric COVID-19 vaccine hesitancy were included. Studies that applied the VHS to understand vaccine hesitancy for vaccines other than pediatric COVID-19 vaccine (e.g., influenza, general childhood vaccines) were excluded. Studies were included if they were inclusive of parents of children 0 to 12 years of age and were published during the COVID-19 pandemic (2019 to current). Included articles had to be written in English language, have an available abstract, be accessible through the University of British Columbia Okanagan (UBCO) library, and be published in a peer-reviewed journal. Review articles, opinion articles and study proposals were excluded from the search. 2.1.2 Information Sources Databases were accessed through the UBCO online library. The search strategy was applied to four databases, including: Embase, Medline, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL). After article screening was completed, the reference lists of articles that met full-text inclusion for this review were screened for additional relevant articles. 2.1.3 Search Strategy Three major concepts derived from the objectives of this review were used to guide the literature search: vaccine hesitancy, COVID-19 vaccines, and pediatric population. The final search strategy included keyword and controlled vocabulary searches for each of the three major concepts, combined through the application of Boolean logic. The combination of keyword and controlled vocabulary searches assisted in capturing any recently published articles on the pandemic that had yet to be assigned controlled vocabulary. Controlled vocabulary differed between databases, resulting in individual application specific to each database, excluding Web 14 of Science, which did not allow for the application of controlled vocabulary. Keyword searches, displayed in Table 1, remained consistent across the four databases to control for these differences in controlled vocabulary between each database. The final search strategy for each database is displayed in Table 2. Table 1 Keyword Search Strategy Vaccine Hesitancy COVID-19 Vaccine Pediatric Population Vaccin* refusal COVID* vaccin* Child* Vaccin* hesita* COVID* immuni* Adolescent Vaccin* avoid* Corona* vaccin* Pediatric Vaccin* delay Corona* immuni* Paediatric Parent* 2.1.4 Selection Process Articles were uploaded to Covidence, a systematic review management software. Covidence automatically screened the set of articles for duplicates. Then, the title and abstracts of the remaining articles were manually screened by a single reviewer against the eligibility criteria. Articles that met the inclusion criteria at the title and abstract level were then screened against the inclusion criteria at full text by the same single reviewer. 2.1.5 Data Items Data was extracted from the included articles on the country of origin, study methods, sample size, data collection timeline, modifications to the VHS, application of the VHS in the study and reported VHS statistics. Significant findings from each study on the factors influencing vaccine hesitancy and vaccine decision-making are discussed using the Vaccine Hesitancy Matrix to explore factors as contextual, individual, or vaccine-specific influences (WHO, 2015). 15 Table 2 Search Strategy by Database Major Concept Search Method Strategy Embase Literature Search Vaccine Hesitancy S1 Subject Heading Vaccine hesitancy/ OR vaccination refusal/ S2 Keyword Vaccin* hesita* OR Vaccin* refusal OR Vaccin* avoid* OR Vaccin* delay S3 Combined S1 OR S2 COVID-19 Vaccine S4 Subject Heading SARS-CoV-2 vaccine/ S5 Keyword COVID* vaccin* OR COVID* immuni* OR Corona* vaccin* OR Corona* immuni* S6 Combined S4 OR S5 Pediatric Population S7 Subject Heading Child/ OR adolescent/ OR pediatrics/ S8 Keyword Child* OR Adolescent OR Parent* OR Pediatric OR Paediatric S9 Combined S7 OR S8 Combined S10 Combined S3 AND S6 AND S9 S11 Combined S10 with limiters: 2019 to current, English language Medline Literature Search Vaccine Hesitancy S1 MESH Terms (MH "Vaccination Refusal+") OR (MH "Vaccination Hesitancy") S2 Keyword Vaccin* hesita* OR Vaccin* refusal OR Vaccin* avoid* OR Vaccin* delay S3 Combined S1 OR S2 COVID-19 Vaccine S4 MESH Terms (MH "COVID-19 Vaccines+") S5 Keyword Search COVID* vaccin* OR COVID* immuni* OR Corona* vaccin* OR Corona* immuni* S6 Combined S4 OR S5 Pediatric Population S7 MESH Terms (MH "Child+") OR (MH "Adolescent") OR (MH "Parents+") OR (MH "Pediatrics+") S8 Keyword Search Child* OR Adolescent OR Parent* OR Pediatric OR Paediatric S9 Combined S7 OR S8 Combined S10 Combined S3 AND S6 AND S9 S11 Combined S10 with limiters: 2019 to current, English language, peer-reviewed 16 Major Concept Search Method Strategy CINAHL Literature Search Vaccine Hesitancy S1 Subject Headings (MH "Vaccination Refusal+") OR (MH "Vaccination Hesitancy") S2 Keyword Vaccin* hesita* OR Vaccin* refusal OR Vaccin* avoid* OR Vaccin* delay S3 Combined S1 OR S2 COVID-19 Vaccine S4 Subject Headings (MH "COVID-19 Vaccines+") S5 Keyword Search COVID* vaccin* OR COVID* immuni* OR Corona* vaccin* OR Corona* immuni* S6 Combined S4 OR S5 Pediatric Population S7 Subject Headings (MH “Child+”) OR (MH “Parents+”) S8 Keyword Search Child* OR Adolescent OR Parent* OR Pediatric OR Paediatric S9 Combined S7 OR S8 Combined S10 Combined S3 AND S6 AND S9 S11 Combined S10 with limiters: 2019 to current, English language, peer-reviewed Web of Science Literature Search Vaccine Hesitancy S1 Keyword Vaccin* hesita* OR Vaccin* refusal OR Vaccin* avoid* OR Vaccin* delay COVID-19 Vaccine S2 Keyword COVID* vaccin* OR COVID* immuni* OR Corona* vaccin* OR Corona* immuni* Pediatric Population S3 Keyword Child* OR Adolescent OR Parent* OR Pediatric OR Paediatric Combined S4 Keyword S1 AND S2 AND S3 S5 Keyword S4 with limiters: 2019 to current, English language 17 2.1.6 Risk of Bias Assessment The Joanna Briggs Institute (JBI) critical appraisal tool for analytical cross-sectional studies was applied to the final set of included studies to assess for risk of bias and quality of articles in this review (JBI, 2017). The JBI checklist is the most preferred quality assessment tool for cross-sectional studies (Ma et al., 2020). The checklist for analytical cross-sectional studies includes eight quality assessment items with a “yes”, “no”, or “unclear” option for each item (JBI, 2017). As done previously in systematic reviews applying a JBI checklist for quality assessment, an overall quality score for each article was calculated (Al-Amer et al., 2022; Villarosa et al., 2019). A yes response on the JBI checklist was scored 1 point and a no or unclear response was scored 0 points. The maximum number of points an article could receive was 8 and the minimum number of points an article could receive was 0. The total number of points for each article out of the total number of items on the checklist assessed was converted into a percentage to represent an overall quality score. Based on previous systematic reviews applying a JBI checklist, an article with a score of 80% or greater was considered high quality, a score of 60% to 79% was considered moderate quality, and a score of less than 59% was considered poor quality (Villarosa et al., 2019). For this review, articles were not excluded based on an overall quality score. 2.2 Results The literature search strategy was applied to Embase, Medline, CINAHL, and the Web of Science in June 2023 and yielded 3929 articles. Figure 1 displays a PRISMA flow diagram depicting the literature screening process. The articles were uploaded to Covidence, and 1411 duplicates were identified and removed by the software. The remaining 2518 articles were screened at title and abstract level and were removed from the dataset (n = 2228) if they did not 18 meet the eligibility criteria. Full text versions of the remaining articles (n=290) were sought for retrieval from the UBCO online library. Articles (n = 2) not accessible at full text from the UBCO online library were excluded after an unsuccessful attempt to secure copies through the UBCO interlibrary loan program. The remaining articles (n = 277) were thoroughly screened at full text against the inclusion criteria by a single reviewer.19 Figure 1 PRISMA Flow Diagram 20 2.2.1 Study Selection A total of 277 articles were excluded during full text screening. The majority of articles were excluded at full text based on their measurement instrument for vaccine hesitancy. Articles (n = 197) were excluded if the primary measurement of vaccine hesitancy was not based on a validated tool. This included articles that assessed COVID-19 vaccine hesitancy through questions on the “willingness” or “likelihood” of a parent accepting a COVID-19 vaccine for their child, survey questions based off theoretical models such as the Health Belief Model, and investigator-created measurement tools or Likert scales. Articles were excluded that measured COVID-19 vaccine hesitancy using the Parent Attitude Towards Childhood Vaccination Scale (n = 28), the 3 C’s or 5 C’s model (n = 7), the Vaccine Confidence Index (n = 4), and other scales (n = 5). Articles were additionally excluded if they were discussion or opinion articles, presentations, or published only as an abstract (n = 19); focused on children older than 12 years of age (n = 4); and were not being peer-reviewed (n = 2). An additional set of articles (n = 10) was excluded that met all inclusion criteria, including the use of the VHS, but did not specifically measure pediatric COVID-19 vaccine hesitancy using the scale. The authors application of the VHS and specific assessment of pediatric COVID-19 vaccine hesitancy for these 10 articles are described in Table 3. After excluding at the full text level, the final sample of articles included 11 quantitative studies. The reference list of the 11 quantitative articles meeting inclusion criteria for this review were screened for additional articles to be included in the review. The screening of reference lists resulted in five additional articles being screened at full text. All five articles were excluded during this full text review based on study-specific assessment of vaccine hesitancy (n = 4) and a discussion article (n = 1). 21 Table 3 Reasons for Full-Text Exclusion Author (Year) Reason for Exclusion A et al. (2021) VHS for adult vaccines; Parent intentions Aldakhil et al. (2021) VHS for childhood vaccines; Study-specific 5-point scale Hammershaimb et al. (2022) VHS for childhood vaccines; Parent likelihood Lachance-Grzela et al. (2022) VHS for childhood vaccines; Parent willingness Li et al. (2022) VHS for adult vaccines; Parent willingness Sarkar et al. (2022) VHS for childhood vaccines; Parent perceptions Srivastava et al. (2022) VHS for childhood vaccines; Electronic vaccination records Wang et al. (2021) VHS for childhood vaccines; Parent willingness Wang et al. (2022) VHS for childhood vaccines; Parent willingness Wang et al. (2023) VHS for childhood and influenza vaccines; Parent willingness 2.2.2 Study Characteristics The final set of articles included 11 cross-sectional studies published between 2020 and 2023. Details on individual study characteristics are described in Table 4. The included studies were conducted in the United States (n=6), Saudi Arabia (n=3), Brazil (n=1), and Mexico (n=1). Applied sampling techniques included convenience (54.5%), snowball (18.2%), probability address-based (9.1%), and a combination of multiple techniques (18.2%). The sample sizes in the studies ranged from 113 to 3167 participants, with five studies having less than 500 participants, three studies having between 500 and 1000 participants, and three studies having greater than 1000 participants. The samples primarily included participants who identified as female or a mother (43.1% to 100%). On average, 28% of participants identified as male or a father, with only one study reporting more participants identifying as male (56.9%) compared to female (Aedh, 2022). Data collection occurred between December 2020 and May 2022, with an average collection period of 3.1 months. All 11 studies measured vaccine intentions rather than actual vaccination behavior. 22 Table 4 Study Characteristics Author Country Method Sample Timeline VHS Modifications VHS Application VHS Scores Aedh (2022) Saudi Arabia Cross-sectional Convenience sampling Snowball sampling 464 February 2022 9-item scale 4-point Likert scale COVID-19 specific language Scoring: A mean score < 3 represents high vaccine hesitancy Prevalence of vaccine hesitancy and associations between VHS score and willingness to accept a COVID-19 vaccine. 72% of parents had a VHS score < 3 (highly hesitant). Al-Qahtani et al. (2022) Saudi Arabia Cross-sectional Convenience sampling 528 February 2022 – March 2022 9-item scale 4-point Likert scale COVID-19 specific language Scoring: A mean score < 3 represents high vaccine hesitancy Prevalence of vaccine hesitancy and associations between VHS score and willingness to accept a COVID-19 vaccine. 55.9% of parents had a VHS < 3 (highly hesitant). Delgado et al. (2022) Mexico Cross-sectional Convenience sampling 699 December 2020 – February 2021 3-item scale 6-point Likert scale COVID-19 specific language Scoring: study specific as part of a larger survey Associations between independent variables and the three statements from the VHS. 56.1% of parents had moderate vaccine hesitancy. 23 Author Country Method Sample Timeline VHS Modifications VHS Application VHS Scores Durkin et al. (2023) United States Cross-sectional Snowball sampling 144 June 2021 – July 2021 9-item scale 5-point Likert scale COVID-19 specific language Scoring: Sub-scale scores averaged, and higher score reflected less favorable safety or efficacy Assess VHS items as potential mediators in relationship between political ideology and willingness to vaccinate a child for COVID-19. Overall vaccine hesitancy not reported. He et al. (2022) United States Cross-sectional Convenience sampling 252 September 202 – February 2021 7-item scale 4-point Likert scale COVID-19 specific language Scoring: Higher item scores indicate higher hesitancy Comparing results from VHS for childhood vaccines and VHS for COVID-19 vaccines. Mean VHS score was 2.07 for COVID-19 vaccines and 2.00 for childhood vaccines. Limbers et al. (2022) United States Cross-sectional Convenience sampling 821 September 2021 – October 2021 10-item scale 4-point Likert scale COVID-19 specific language Scoring: No specific scoring system stated Associations between willingness to accept a COVID-19 vaccine and VHS responses. Overall vaccine hesitancy not reported. 24 Author Country Method Sample Timeline VHS Modifications VHS Application VHS Scores Martinez et al. (2022) Brazil Cross-sectional Convenience sampling 1007 March 2022 – May 2022 10-item scale 5-point Likert scale COVID-19 specific language Scoring: Overall score ranged from 0 to 40 with a higher score representing higher vaccine hesitancy Associations between independent variables and VHS scores. Mean VHS scores for parents identifying as male was 14.83 and females was 13.70. Skeens et al. (2022a) United States Cross-sectional Convenience and purposive sampling 113 February 2021 – July 2021 8-item scale 4-point Likert scale COVID-19 specific language Scoring: Average score from six statements with a lower score representing vaccine hesitancy on a scale from 1 to 4 Comparison of overall VHS scores between parent groups. Mean VHS score was 2.87. 25 Author Country Method Sample Timeline VHS Modifications VHS Application VHS Scores Skeens et al. (2022b) United States Cross-sectional Convenience sampling 491 February 2021 – May 2021 8-item scale 4-point Likert scale COVID-19 specific language Scoring: Average score with a lower score representing vaccine hesitancy on a scale from 1 to 4 Correlation of independent variables and VHS scores. The mean VHS score was 2.92. Szilagyi et al. (2021) United States Cross-sectional Probability-based sampling 1745 February 2021 – March 2021 9-item scale 4-point Likert scale COVID-19 specific language Scoring: No specific scoring system stated Associations between VHS items and parent’s likeliness to vaccinate their child for COVID-19. Overall vaccine hesitancy not reported. Temsah et al. (2021) Saudi Arabia Cross-sectional Snowball sampling. 3167 Not reported. 8-item scale 4-point Likert scale COVID-19 specific language Scoring: A mean score < 3 represents high vaccine hesitancy Comparing results of findings from routine childhood VHS and COVID-19 VHS. Mean VHS score was 2.63 for COVID-19 vaccines and 2.98 for childhood vaccines. 26 2.2.2.1 Application of Vaccine Hesitancy Scale The version, modification, and application of the VHS differed across the included studies. Modifications to the VHS were based on the original VHS by Larson et al. (2015) in four studies, the modified VHS for child and influenza vaccines by Kempe et al. (2020) in three studies, the original validated VHS by Shapiro et al. (2018) in two studies, and the modified VHS for child, influenza, and HPV vaccines by Helmkamp et al. (2021) in two studies. Scales applied in the included studies ranged from 3-item to 10-item scales. The 4-point Likert scale was most used (66.6%), followed by the 5-point Likert scale (18.2%) and a 6-point Likert scale (9.1%). An overall scoring system for the scale was not reported in three of the 11 studies (Delgado-Gallegos et al., 2021; Limbers & Thompson, 2022; Szilagyi et al., 2021). Scoring metrics differed across included studies. For the eight studies that did associate a VHS score with a degree of vaccine hesitancy, a lower score on the VHS represented greater vaccine hesitancy in five studies and a higher score on the VHS represented greater vaccine hesitancy in three studies. Due to the differences in modifications to the VHS and scoring systems, it was not possible to compare overall VHS scores across the studies. Reported statistics related to VHS scores specific to each study are displayed in Table 4. All 11 studies modified language within the VHS in a similar manner to be specific to COVID-19 vaccines. For example, the statement “I am concerned about serious adverse effects of vaccines” from the original VHS by Larson et al. (2015) was modified to “I am concerned about serious adverse effects of a COVID-19 vaccine” (Aedh, 2022; Szilagyi et al., 2021). In addition to subtle language changes, some studies completely omitted questions or added additional questions to their versions of the VHS (Al-Qahtani et al., 2022; Limbers & Thompson, 2022). External validation of the modifications to the VHS was not reported in any of included studies. Five studies justified the internal reliability 27 of their surveys by reporting Cronbach’s alpha (Aedh, 2022; Al-Qahtani et al., 2022; Delgado-Gallegos et al., 2021; Skeens et al., 2022b; Temsah et al., 2021). Cronbach’s alpha was reported on entire study surveys in the majority of these studies, with only two reporting the statistics specific to modifications to the VHS (Skeens et al., 2022b; Temsah et al., 2021). The reported Cronbach’s alpha across the five studies ranged from 0.72 to 0.93. These values are within the recommendations of 0.7-0.95 (Bland & Altman, 1997; Tavakol & Dennick, 2011). The application of the VHS differed across the set of studies and included using the VHS to report the overall prevalence of pediatric COVID-19 vaccine hesitancy, analyzing relationships between overall scores or individual statements on the VHS and independent variables (e.g., demographics) within the study, and analyzing the relationship of VHS scores and parental acceptance of pediatric COVID-19 vaccines. 2.2.3 Quality Assessment of Studies The results from the quality assessment of included studies using the JBI checklist for analytical cross-sectional studies are displayed in Table 5. Based on previous quality scoring reported in the literature, the final set of 11 articles includes one high-quality (score > 80%), seven moderate-quality (score 60-79%), and three poor-quality (score < 59%) studies (Villarosa et al., 2019). Relatively consistent scoring across the reviewed studies was noted. All studies received a yes response to the question “Were the study subjects and the setting described in detail?” except for the study by Temsah et al. (2021) which did not describe a timeline for the study, a critical component to a context-specific topic such as vaccine hesitancy. All studies received a yes response to the question “Were the outcomes measured in a valid and reliable way?” except for the study by Delgado-Gallegos et al. (2021) which did not use a complete version of the VHS. All studies received a yes response to the question “Was appropriate 28 statistical analysis used?” except for the study by Skeens et al. (2022a) which could have been strengthened by running a regression between variables of interest and vaccine hesitancy scores instead of limiting the analysis to Kruskal-Wallis and Wilcoxon rank-sum tests. Three studies received a no response for the question “Were objective, standard criteria used for measurement of the condition?” for not describing a scoring system for the VHS to objectively describe a parent’s vaccine hesitancy (Delgado-Gallegos et al., 2021; Limbers & Thompson, 2022; Szilagyi et al., 2021). Three studies received a no response for the question “Were strategies used to deal with confounding factors stated?” for not clearly articulating methods, such as using regression models, to mitigate risks of confounding (Durkin et al., 2023; He et al., 2022; Skeens et al., 2022a). Quality was negatively impacted across the set of studies for questions “Were criteria for inclusion in the sample clearly defined?”, “Was the exposure measured in a valid and reliable way?”, and “Were confounding factors identified?” Studies that did not clearly report both an inclusion and exclusion criteria for their sample received a no response for “Were criteria for inclusion in the sample clearly defined?” For the question, “Was the exposure measured in a valid and reliable way?”, most studies did not use a validated survey instruments to measure independent variables used in the final analyses to determine contributing factors of vaccine hesitancy. Most studies received a no response for “Were confounding factors identified?” as only two studies explicitly reported identifying confounding factors (Martinez et al., 2022; Skeens et al., 2022b). 29 Table 4 Critical Appraisal Results Aedh (2022) Al-Qahtani et al. (2022) Delgado-Gallegos et al. (2022) Durkin et al. (2023) He et al. (2022) Limbers et al. (2022) Martinez et al. (2022) Skeens et al. (2022a) Skeens et al. (2022b) Szilagyli et al. (2021) Temsah et al. (2021) Criteria Were the criteria for inclusion in the sample clearly defined? N N N Y Y Y N Y Y N N Were the study subjects and the setting described in detail? Y Y Y Y Y Y Y Y Y Y N Was the exposure measured in a valid and reliable way? N N N N N N N Y Y N N Were objective, standard criteria used for measurement of the condition? Y Y N Y Y N Y Y Y N Y Were confounding factors identified? N N N N N N Y N Y N N Were strategies to deal with confounding factors stated? Y Y Y N N Y Y N Y Y Y Were the outcomes measured in a valid and reliable way? Y Y N Y Y Y Y Y Y Y Y Was appropriate statistical analysis used? Y Y Y Y Y Y Y N Y Y Y Total Quality Score 63 63 38 63 63 63 75 63 100 50 50 30 2.2.4 Main Findings For studies that reported overall pediatric COVID-19 vaccine refusal, 18% to 42% of parents were unlikely or not willing to vaccinate their child for COVID-19 (Limbers & Thompson, 2022; Skeens et al., 2022b; Szilagyi et al., 2021; Temsah et al., 2021). Despite differences discussed previously in how studies applied the VHS, there were similarities among the influential factors reported to have significant associations with perceptions towards pediatric COVID-19 vaccines. The contextual, individual, and vaccine-specific factors described in the WHO Vaccine Hesitancy Matrix were used to categorize reported findings from the included studies. 2.2.4.1 Contextual Influences Contextual influences on vaccine hesitancy include socioeconomic, environmental, cultural or historical factors and were discussed in all 11 included studies (WHO, 2015). Socioeconomic indicators, including education and household income, were considered significant factors in vaccine decision-making. In six studies, parents with higher levels of education or higher household incomes were more willing to accept a COVID-19 vaccine for their children or had lower rates of vaccine hesitancy than parents with lower levels of education and household incomes (Aedh, 2022; Al-Qahtani et al., 2022; Durkin et al., 2023; He et al., 2022; Skeens et al., 2022a; Szilagyi et al., 2021). In contrast to these findings, Temsah et al. (2021) found that parents with lower levels of education were significantly more likely to accept a pediatric COVID-19 vaccine. Differences between parents’ reported gender or relationship with the child were also discussed as a significant influencer on vaccine hesitancy. In some studies, parents who identified as a child’s mother were more likely to refuse COVID-19 vaccination for their child than those identifying as fathers (Al-Qahtani et al., 2022; He et al., 31 2022). In contrast to these findings, Skeens et al. (2022b) found that male parents had significantly higher rates of vaccine hesitancy than female parents. When considering this difference, it is important to note that 10% of included participants in the study by Skeens et al. (2022b) were male. Contextual influences on pediatric COVID-19 vaccine hesitancy identified in some studies did not remain significant in others. For example, Martinez et al. (2022) found that a parent’s sex did not have any significant association with vaccine hesitancy. Other contextual influences such as political ideology, age, race and geographical location were reported as factors that influence pediatric COVID-19 vaccine hesitancy across the included studies (Delgado-Gallegos et al., 2021; Durkin et al., 2023; He et al., 2022; Limbers & Thompson, 2022; Martinez et al., 2022) 2.2.4.2 Individual Influences Individual influences on vaccine hesitancy include an individual’s past health experiences, health belief systems, or impacts of the social environment on health decision-making and were discussed in nine of the included studies (WHO, 2015). Childhood immunization history was reported to significantly influence vaccine hesitancy. Routine childhood immunization history was reported to be associated with pediatric COVID-19 vaccine hesitancy, where parents of unvaccinated children for childhood vaccines were more likely to be vaccine hesitant than parents of children up to date with their childhood immunizations (Aedh, 2022; Al-Qahtani et al., 2022; Martinez et al., 2022). In studies comparing routine VHS scores to COVID-19 VHS scores, parents had higher vaccine hesitancy toward pediatric COVID-19 vaccines (He et al., 2022; Temsah et al., 2021). In addition, influenza and parental COVID-19 vaccination history demonstrated significant relationships with pediatric COVID-19 vaccine hesitancy. Unvaccinated parents or parents of children not vaccinated for influenza were more 32 likely to refuse a COVID-19 vaccine for their child that those who were vaccinated (Delgado-Gallegos et al., 2021; Szilagyi et al., 2021). Similarly, parents who were not vaccinated for COVID-19 had higher vaccine hesitancy for pediatric COVID-19 vaccines than those who were vaccinated (Martinez et al., 2022; Skeens et al., 2022a; Szilagyi et al., 2021; Temsah et al., 2021). Finally, child health status had a significant impact on a parent’s willingness to accept a COVID-19 vaccine. Parents of children not diagnosed with a chronic disease were reported to be significantly more willing to accept a COVID-19 vaccine for their child (Aedh, 2022). In a study of parents of children with cancer, parents of children actively receiving treatment had higher vaccine hesitancy (Skeens et al., 2022b). In contrast to these studies, Al-Qahtani et al. (2022) reported that parents of children with chronic illness or comorbidities were more willing to vaccinate their children for COVID-19 compared to parents of children without chronic illness. 2.2.4.3 Vaccine-specific Influences Vaccine-specific influences on vaccine hesitancy include factors related to vaccine manufacturing, vaccine administration, or HCP vaccine recommendations and were discussed in eight of the included studies (WHO, 2015). Discussions on vaccine-specific influences were most commonly on efficacy and safety concerns due to the novelty of the COVID-19 vaccine. The majority of parents agreed with the statement on the VHS that they were concerned with the potential adverse effects of a COVID-19 vaccine for their child (Aedh, 2022; Al-Qahtani et al., 2022; Martinez et al., 2022; Skeens et al., 2022a; Skeens et al., 2022b; Szilagyi et al., 2021; Temsah et al., 2021). Greater concern about adverse effects from the COVID-19 vaccine was associated with a decreased likelihood of accepting a COVID-19 vaccine for their child (Aedh, 2022; Szilagyi et al., 2021). Some parents expressed that they were more concerned about the side effects for their children than for themselves receiving an adult COVID-19 vaccine (Skeens 33 et al., 2022b). Parents who reported that they have trust in the COVID-19 vaccine development process were more likely to indicate a willingness to accept a pediatric COVID-19 vaccine for their child (Limbers & Thompson, 2022). A child’s doctor or HCP was commonly reported as a trustworthy source for information on pediatric COVID-19 vaccines for parents (Aedh, 2022; Limbers & Thompson, 2022; Szilagyi et al., 2021). Parents had less vaccine hesitancy when the child’s HCP recommended children receive a COVID-19 vaccine (Skeens et al., 2022a). 2.3 Discussion This literature review demonstrated that there are global similarities in trends among parents making vaccine decisions for their children. The sample of included articles in this review represents a homogenous sample of cross-sectional studies that allowed for a rapid assessment of COVID-19 vaccine hesitancy during an emerging public health crisis. The results of the studies included in this review need to be considered with their research design and quality indicators in mind. After applying the JBI checklist, most of the included studies were rated as moderate-quality (63.6%) or poor-quality (27.3%) studies. This is reflective of a group of cross-sectional studies with similar sampling, analysis, and reporting methods. The quality of the set of articles was most impacted by a lack of transparency in sampling, not measuring exposures in a valid and reliable way, and not identifying confounding factors in their analysis. Quickly modifying the VHS to be specific to pediatric COVID-19 vaccines without validation during a pandemic was a reasonable decision by the authors. The only consistent modification to the VHS across the set of articles was changing language about general vaccines to COVID-19 vaccines. Other features of the scale, including the number of items on a scale, the scoring system, and the application of the Likert scale varied across the included studies. These differences were related to the authors modifying different versions of the VHS and made it very 34 difficult to make direct comparisons on the overall prevalence of vaccine hesitancy across the reviewed studies (Helmkamp et al., 2021; Kempe et al., 2020; Larson et al., 2015; Shapiro et al., 2018). Despite using an objective scale to quantify vaccine hesitancy, some studies conducted an analysis on parents’ yes or no responses on their willingness or likelihood of accepting a COVID-19 vaccine (Aedh, 2022; Al-Qahtani et al., 2022; Durkin et al., 2023; Limbers & Thompson, 2022; Szilagyi et al., 2021). Doing so created blurred lines in an author’s objective definition of vaccine hesitancy, leading to questions if vaccine hesitancy is defined by the VHS or by a parent’s willingness to accept a COVID-19 vaccine. In the future, studies should rely on the strength of the VHS providing an objective understanding of the complex phenomenon of vaccine hesitancy. Additionally, validation of the modifications to the VHS should be completed to determine if the minor changes to the scale significantly affected its reliability and validity. This would justify modifying the VHS for future pandemics or epidemics demanding prompt research on vaccine decision-making. The findings on factors influencing vaccine decision-making for pediatric COVID-19 vaccines have corroborated the previous work of the SAGE Working Group on Vaccine Hesitancy. The defining attributes of vaccine hesitancy being context-specific, multifactorial, and non-directional are exemplified in the findings of this literature review (MacDonald & The SAGE Working Group on Vaccine Hesitancy, 2015). The context-specific nature of vaccine hesitancy was demonstrated with the example of parents' reported gender influencing vaccine hesitancy. Skeens et al. (2022b) reported male parents had higher associations with vaccine hesitancy than female parents in their sample. Despite this report, Martinez et al. (2022) found no significant association between parents’ reported gender and vaccine hesitancy. The differences in these reports demonstrated that influences of parents’ reported gender on vaccine hesitancy 35 differ between settings, highlighting the importance of context when drawing conclusions on determinants of vaccine hesitancy. The non-directional nature of vaccine hesitancy was also demonstrated by inconsistencies in the findings between studies. For example, the contextual factor education in the Vaccine Hesitancy Matrix was both protective and predictive of vaccine hesitancy in this sample of articles. In some settings, higher educational attainment was associated with decreased prevalence of vaccine hesitancy (Aedh, 2022; Durkin et al., 2023). In contrast to these findings, higher education was associated with an increased prevalence of vaccine hesitancy and lower educational attainment was associated with vaccine acceptance (Temsah et al., 2021). Therefore, a general conclusion could not be made on the relationship between educational attainment and pediatric COVID-19 vaccine hesitancy. Interestingly, education was discussed as a non-directional factor in some of the seminal work during the development of the Vaccine Hesitancy Matrix (Larson et al., 2014). When comparing differences between the studies included in this review, it was clear that the contextual nature of vaccine hesitancy underpins its non-directional characteristic. It was difficult to compare differences in key socioeconomic variables, such as education, across multiple studies vastly differing by geography and cultural norms. Finally, the multifactorial property of vaccine hesitancy was demonstrated by multiple studies reporting significant findings in contextual, individual, and vaccine-specific categories of the Vaccine Hesitancy Matrix (Aedh, 2022; Martinez et al., 2022; Szilagyi et al., 2021). For example, Aedh (2022) reported significant associations in all three categories of the matrix. Parent concerns for adverse effects from the COVID-19 vaccine, the health status of a parent’s child, and a parent’s household income were all identified as factors significantly affecting a parent’s willingness to vaccinate their child for COVID-19 in the same sample of parents (Aedh, 2022). This example exemplifies the complexity of vaccine hesitancy 36 by demonstrating that an individual parent or group of parents can experience multiple determinants of vaccine hesitancy. This example also highlights the challenges of identifying specific target groups of parents for vaccine promotion, given the likelihood of blurred lines between parental characteristics most influencing vaccine hesitancy. 2.3.1 Limitations The limitations of this systematic review should be considered when interpreting its findings. The entire review process, including the JBI quality assessment, was conducted by a single reviewer. The lack of a triangulation process in this review may have resulted in selection bias throughout the article screening and critiquing process. The findings of this review should also be interpreted with an awareness that most of the included studies in this review included an American sample of parents and that none of the included studies included a sample of Canadian parents. This may impact the ability to generalize results when making comparisons from this literature review to the findings of this thesis. 2.3.2 Applicability to Current Study The findings from this literature review supported the methodological plan for this thesis. Reviewing the variations in the application of the VHS highlighted strategies to utilize in this thesis. This thesis relied on the strengths of the studies that more closely aligned their modifications to previously validated VHS. When applying the VHS in the analysis for this thesis, the number of items on the scale, cut-off points, and style of the Likert scale remained consistent with previously validated versions of the VHS. Additionally, this thesis relied on the strength of the VHS and the foundational work of the SAGE Working Group on Vaccine Hesitancy, using the VHS score as the primary metric for vaccine hesitancy. The findings from this review on determinants of pediatric vaccine hesitancy from the Vaccine Hesitancy Matrix 37 helped to identify relevant independent variables included in the analysis for this study. Factors that were identified across the set of articles, including education, household income, immunization history, child health status, and HCP vaccine recommendations were included in the analysis for this study. 38 Chapter 3: Methodology In this chapter I present the methodological strategy for the study. This study used a quantitative research design to analyse a portion of previously collected data on COVID-19 vaccine perceptions and concerns among people in B.C. The design of the study, participants, recruitment strategy, data collection process, surveying procedure, data analysis plan, sample size justification, and ethical considerations are discussed in full throughout this chapter. 3.1 Study Design This study applied a cross-sectional study design. Cross-sectional designs are used to provide a snapshot of the prevalence of phenomenon at one point in time (Duckett, 2021; Wang & Cheng, 2020). A cross-sectional design was appropriate for a study measuring vaccine hesitancy at one point during the COVID-19 pandemic in B.C. A single data collection period in a cross-sectional design allowed for relatively quick study completion and low ethical burdens (Wang & Cheng, 2020). This makes the design very suitable for use in emergencies such as pandemics, as it allowed the research team to develop a rapid understanding of challenges ahead, such as planning for a mass-immunization program. Cross-sectional studies can be analytical in design and look at relationships between variables and outcomes (Omair, 2015; Wang & Cheng, 2020). The current study analyzed the relationship between determinants of vaccine hesitancy from the Vaccine Hesitancy Matrix (e.g., sociodemographic characteristics, immunization history, HCP vaccine recommendations) and the prevalence of vaccine hesitancy (WHO, 2014). It is important to note that cross-sectional studies are limited to determining associations, meaning that this study was not able to determine causal relationships between factors in the Vaccine Hesitancy Matrix and vaccine hesitancy (Omair, 2015). 39 3.2 Study Participants Participants for this study included a sub-sample from a larger study on COVID-19 vaccine intentions in B.C. The larger study included responses from (1) people who were planning to become pregnant, (2) people who were pregnant or breastfeeding, and (3) people who were parents of children under 12 years of age. Eligibility requirement for participation in the larger study included (a) people living in B.C., (b) people aged 18 years or older, (c), people who could communicate effectively in English, and (d) people who had access to an internet-connected device. Exclusion criteria for participation in the larger study included (a) people who had current major mental health conditions or mood disorders (e.g., schizophrenia, bipolar disorder, severe depression) and (b) people who had given birth with major pregnancy-related complications (e.g., severe pre-eclampsia, premature birth before 26-week gestation, birth weight under 1000 grams, severe medical conditions) within the last 12 months. This study only included data from the sub-sample of people who self-identified as parents to any number of children under the age of 12 and who met all eligibility requirements of the larger study. 3.3 Participant Recruitment Participants were primarily recruited through convenience sampling methods via poster advertisements in partnering public agencies in the interior region of B.C. (e.g., Interior Health Authority, The Bridge Youth and Family Services, Kelowna Community Resources). Additional participant recruitment strategies included snowball sampling via word-of-mouth from previous participants and advertisements through social media, publicly accessible maternal or child health forums, and existing programs for families in B.C. 40 3.4 Sample Size Calculation The sample size calculation for this study was based on the primary objective of determining the prevalence of vaccine hesitancy among a sample of parents of children under the age of 12 in B.C. The calculation was based on a formula by Pourhoseingholi et al. (2013) for sample size determination in cross-sectional studies on prevalence. The z statistic 1.96 was used for a 95% level of confidence. The prevalence selected for the calculation was based on a meta-analysis of parental acceptance of pediatric COVID-19 vaccines, which found the average global vaccine acceptance among parents to be 60% (Chen et al., 2022). For this calculation, the alternative of the acceptance rate, 40%, was used as an estimate of prevalence for pediatric COVID-19 vaccine hesitancy. A precision of 5% was selected based on the prevalence falling between 10% and 90% (Pourhoseingholi et al., 2013). This means that based on the formula by Pourhoseingholi et al. (2013), 369 parents were needed for this study. A second calculation was conducted with a prevalence of 50% to represent a more conservative sample size calculation assuming maximum population variability, which, when applied to the formula resulted in a required sample size of 384 parents (Israel, 1992). Given that the sample for this study was already known since that the data for this study has previously been collected, the sample of parents from the larger study far exceeded the sample size requirements under both assumptions. 3.5 Data Collection People who were eligible to participate in the study were asked to complete an online survey between October 2021 and December 2021 using Qualtrics. During this timeframe, there was a cumulative total of approximately 75,000 cases of COVID-19 in B.C., with the Delta variant making up 99.9% of positive cases in the province (BCCDC, 2021; BCCDC, 2024). Figure 2 displays a graph depicting the 7-day average of COVID-19 cases in B.C. from January 41 2021 to January 2022 with the surveying period for this study highlighted (BCCDC, 2024). Participants completed the survey at any time or location with reliable internet access during the 3-month surveying period. To begin the survey, participants had to read and agree to an information and consent form for the larger study prior to accessing any further survey questions. After consenting to participate, survey questions were generated based on the sub-sample(s) groups that participants self-identified with (e.g., pre-pregnant, pregnant or breastfeeding, or parent to child under 12 years of age). An individual participating in the study could belong to one, two, or all three sub-samples. Figure 2 Average Case Count During Surveying Period Source: (BCCDC, 2024) 42 3.6 Survey Procedure This study only used the data from the larger study specific to participants study who self-identified as a parent with one or more children under the age of 12. The survey specific to parents included a maximum of 48 questions and took an average of 15 to 20 minutes to complete. The survey questions were divided into five sections: personal and child health; COVID-19 history, exposures, and risk perceptions; COVID-19 vaccine knowledge, confidence, and acceptance; COVID-19 and vaccine hesitancy scales; and demographic information. Most questions in the survey used a branching logic design, where depending on a yes/no response, more specific questions were generated. For that reason, not all participants were required to answer all 48 questions. Participants were able to complete sections of the survey for each of their children, up to a maximum of four children. 3.7 Study Measures The primary outcome variable for this study, vaccine hesitancy, was measured using the investigator modified version of the WHO VHS included in the study survey. The selection of included covariates in the analysis was based on factors identified in the systematic review reported to have significant associations with pediatric COVID-19 vaccine hesitancy in other settings. The variables aligned with the overarching categories of contextual, individual, and vaccine-specific factors that comprise the Vaccine Hesitancy Matrix (WHO, 2015). Only the survey questions pertinent to the analysis in this study are discussed in full. 3.7.1 Outcome Variable - Vaccine Hesitancy Vaccine hesitancy, measured using the VHS, was a key variable in this study. Similar to the findings in the literature review, the survey included investigator modifications to a previously validated VHS, since this survey was developed at a time when no available validated 43 measurement tools specific to pediatric COVID-19 vaccines existed. Included in the survey were items inclusive of all of the items used in the original VHS by Larson et al. (2015) and development of vaccine-specific scales by Helmkamp et al. (2021), with language modified to be specific to the COVID-19 vaccine. Only nine items that were most reflective of items included in final modified and validated versions of the VHS were included in the analysis for this study (Helmkamp et al., 2021; Larson et al., 2015; Shapiro et al., 2018). The nine items that were included in the analysis for this study include: 1. The COVID-19 vaccine will be important for my child’s health. 2. Getting the COVID-19 vaccine will be a good way to protect my child from COVID-19 infection. 3. The COVID-19 vaccines for children will be effective. 4. Having my child vaccinated against COVID-19 will be important for the health of others in my community. 5. The COVID-19 vaccine will be beneficial for my child. 6. I will do what my child’s HCP and public health officials recommend about the COVID-19 vaccine. 7. The COVID-19 vaccine has not been around long enough to be sure that is safe for children. 8. I am concerned about serious side effects of a COVID-19 vaccine for my child. 9. I think the COVID-19 vaccine might cause lasting health problems for my child. Two sub-scales continued to be reflected in the modified VHS (Helmkamp et al., 2021; Shapiro et al., 2018). Item 1 through item 6 comprised the 6-item “lack of confidence” sub-scale and item 7 through item 9 comprised the 3-item “risk” sub-scale. The 4-point Likert scale 44 without a neutral point was applied to this study, aligning with the work of Kempe et al. (2020) and Helmkamp et al. (2021), based on the argument that the neutral point increases social desirability bias (Chyung et al., 2017). Response options to each item on the scale included strongly agree, somewhat agree, somewhat disagree, and strongly disagree with respective scores 1, 2, 4, and 5 (Helmkamp et al., 2021). Item 7, item 8, and item 9 on the scale were reverse scored so that a higher score always represented greater hesitancy. A total mean score was used to determine if a participant was vaccine hesitant. The mean score for the modified VHS was calculated by totaling the scores associated with a participant’s responses for the nine items in the scale and then dividing the total score by 9. The mean score for the lack of confidence sub-scale was calculated by totaling the scores associated with a participant’s responses for the first six items on the scale and then dividing this score by 6. The mean score for the risk sub-scale was calculated by totaling the scores associated with a participant’s responses for the last three items on the scale and then dividing this score by 3. The mean scores for the modified VHS, lack of confidence sub-scale, and risk sub-scale could range from 1 to 5 and were compared to the previously identified cut-off point of 3 that defines vaccine hesitancy. The same cut-off of 3 was used to determine if a parent lacked confidence or had increased risk perception in the analyses using the two sub-scales. A participant with a mean score greater than 3 was considered vaccine hesitant for the total scale, to have decreased confidence for the lack of confidence sub-scale, and to have increased risk perception for the risk sub-scale (Helmkamp et al., 2021; Kempe et al., 2020). 3.7.2 Covariates – Contextual Factors Variables of interest for contextual influences on vaccine hesitancy from the Vaccine Hesitancy Matrix included education, income, age, and number of children under 12 years of 45 age. Participant race was not included as a covariate in the analysis given that 80% of respondents reported being White, with 2% or fewer participants selecting any one of the other races included in the survey. Study participants were asked to report their highest educational attainment. Response options included less than grade 12, completed grade 12, attended but did not complete post-secondary, completed college or technical diploma, completed university bachelor’s degree, and completed postgraduate school or more. The variable education was recoded to grade 12 or less, certificate or college diploma, bachelor’s degree, and post-graduate degree or more. Study participants were asked to report their annual household income. Response options were less than $30,000, $30,000 to $59,999, $60,000 to $89,999, $90,000 to $119,999, $120,000 to $149,999, $150,000 or more. The variable income was recoded to less than $60,000, $60,000 to $89,999, $90,000 to $149,999, and $150,000 or greater to most closely reflect Canadian federal income tax brackets (Government of Canada, 2023b). Study participants were asked to select their age category in years. Response options included younger than 20, 20 to 29, 30 to 39, 40 to 49, 50 to 59, and over 60. The variable age was recoded to younger than 30, 30 to 39, and 40 or older since only 8 participants reported their age 50 to 59 and no participants reported their age less than 20 or 60 and older. Study participants were asked to report the number of children in their household under the age of 12 years. 3.7.3 Covariates – Individual Factors Variables of interest for individual influences on vaccine hesitancy from the Vaccine Hesitancy Matrix included parent COVID-19 vaccination status, child influenza immunization status, and child health status. Study participants were asked “Have you been vaccinated against COVID-19?” Response options were no, yes – first dose only, yes – both first and second dose, or prefer not to answer. Participants were recoded yes if they responded yes – first dose only or 46 yes – both first and second dose. Participants who responded prefer not to answer were recoded as no. For each child, study participants were asked “Has the child received the influenza vaccine within the last 12 months?” with response options yes/no. Participant responses were coded yes if they responded yes to any one of their children being immunized for influenza in the last year. For each child, study participants were asked “Has this child ever been diagnosed with any chronic or ongoing health problems?” with response options yes/no. Participant responses were coded as yes if they responded yes to any one of their children being diagnosed with a chronic illness. 3.7.4 Covariates – Vaccine-Specific Factors The variable of interest for vaccine-specific influences on vaccine hesitancy from the Vaccine Hesitancy Matrix was HCP vaccine recommendations. Study participants were asked if a doctor, nurse, or HCP recommended that their children get the COVID-19 vaccine when it becomes available. This variable was coded as yes/no. 3.7.5 Additional Variable of Interest The variable vaccine intention was used as an outcome variable to determine the clinical significance of the VHS. Participant reported pediatric COVID-19 vaccine uptake was inconsistent between participants given that some participants completed the survey before the approval of vaccines for children aged 5 to 11 years of age or were responding to the survey for a child under 5 years of age when vaccines were not approved in B.C. (Government of British Columbia, 2021; Government of British Columbia,2022). For those reasons, vaccine intentions were a more consistent measurement between all study participants. Parents were asked, “If a COVID-19 vaccine becomes available for children under 12 years of age, will you vaccinate your child?” Response options were definitely will not, highly unlikely, somewhat unlikely, 47 unsure or undecided, somewhat likely, highly likely, definitely will. Participant responses were coded yes for vaccine intention if they answered somewhat likely, highly likely, and definitely likely for any of their children under 12 years of age. Participants who responded unsure, somewhat unlikely, highly unlikely, and definitely not for all their children under 12 years of age were coded as no for vaccine intention. 3.8 Data Analysis Plan The survey data was downloaded from Qualtrics into Stata for analysis (StataCorp, 2023). The data analysis plan includes a discussion of the procedure for data cleaning and the statistical analysis plan to meet the three objectives of this study. 3.8.1 Missing Data There are multiple levels of missing data and strategies to deal with missing data (Bannon Jr, 2015; Hawthorne & Elliot, 2005). Despite differences in opinions on strategies for dealing with missing data, there is a shared consensus that it is prudent to be transparent about missing data and how it will be treated (Bannon Jr, 2015; Downey & King, 1998; Hawthorne & Elliot, 2005). After the data was uploaded to Stata, missing data was identified. Parents with three or more missing items in the demographic section of the survey were removed from the dataset. For parents with one or two missing items in the demographic section, missing categorical variables were replaced with the most frequent response for the survey item and missing continuous variables were replaced with the median value for the survey item. Since the VHS is a key variable in the analysis for this study, a clear strategy for missing items within the VHS was also needed. When dealing with missing data in a Likert scale, better representation of an original data set is maintained when there is less than a 20% margin of missing data (Downey & King, 1998). For that reason, for the nine modified VHS items, parents with three or more missing 48 items (i.e., 33% or greater missing scale items) were removed from the dataset. Substitution methods, including item mean substitution and personal mean substitution are considered appropriate strategies for treating missing data in Likert scales (Downey & King, 1998). When the two methods were compared, personal mean substitution was found to be superior to item mean substitution (Hawthorne & Elliot, 2005). For that reason, personal mean substitution was applied to this study for parents with one or two missing items in the modified VHS and involved replacing an individual’s value for a missing scale item with the mean score for that individual’s completed scale items (Downey & King, 1998). 3.8.2 Statistical Analysis Statistical analysis in this study included descriptive statistics and logistic regression analysis. Frequencies were used to describe the sample of parents responding to the survey including their age, number of children, gender, education level, household income, and ethnicity. Additionally, the number of parents who have tested positive for COVID-19, vaccination status of parents, number of children who have tested positive for COVID-19, and reasons for parent’s likelihood or unlikelihood of intending to accept a COVID-19 vaccine for their child were described. 3.8.2.1 Prevalence of Vaccine Hesitancy The prevalence of COVID-19 vaccine hesitancy among study participants was determined by calculating the percentage of parents with a total mean VHS score greater than 3, based on the pre-defined cut-off point (Helmkamp et al., 2021). This was completed for the modified VHS, the lack of confidence sub-scale, and the risk sub-scale. Study participants with a total mean VHS score greater than 3 were considered hesitant for the 9-item scale. Study participants with a total mean VHS score greater than 3 were considered to have decreased 49 confidence for the 6-item lack of confidence sub-scale. Study participants with a total mean VHS score greater than 3 were considered to have increased risk perception for the 3-item risk sub-scale. 3.8.2.2 Predictors of Vaccine Hesitancy Logistic regression was used to identify contextual, individual, and vaccine-specific factors that were associated with COVID-19 vaccine hesitancy among parents of children under 12 years of age in B.C. An assumption of logistic regression analysis is that the dependent variable is binary (Kellar & Kelvin, 2013). The findings of the VHS were treated as a dichotomous variable, with participants coded as hesitant (mean VHS > 3) and not hesitant (mean VHS < 3). Previous analyses using similarly modified versions of the VHS have also treated the results of the scale as a nominal variable (Helmkamp et al., 2021; Temsah et al., 2021). This aligned with the argument that a 4-point Likert scale should not be treated as an interval level of measurement (Chyung et al., 2017). A total mean VHS score was calculated and dichotomized for the modified VHS, the lack of confidence sub-scale, and the risk sub-scale. Independent variables in logistic regression can be of any measurement scale and included determinants from the WHO Vaccine Hesitancy Matrix (Kellar & Kelvin, 2013; WHO, 2015). A logistic regression analysis was completed for the modified VHS, the lack of confidence sub-scale, and the risk sub-scale. An unadjusted logistic regression analysis between each independent variable from the Vaccine Hesitancy Matrix and the outcome variable, vaccine hesitancy, was conducted. For the unadjusted analysis, a statistically significant relationship between an independent variable and vaccine hesitancy was defined as p < .2 (Hosmer et al., 2013). The decision to compare p-values to .2 was based on recommendations in the literature to use higher significance levels (i.e., .2 – .25) for an unadjusted analysis rather than traditional 50 values of p <.05, to avoid excluding potential variables of importance from the multivariable model (Hosmer et al., 2013). Independent variables with p < .2 were included in the adjusted multivariable logistic regression models. To avoid re-categorization of variables, if one category of a variable is significant, the entire original variable was carried forward into the adjusted models (Hosmer et al., 2013). An adjusted multivariable logistic regression model was completed for the modified VHS, the lack of confidence sub-scale, and the risk sub-scale. In the adjusted models, a p-value <.05 was considered statistically significant. The odds ratio (OR), 95% confidence interval (CI) and p-value were reported for both the unadjusted and adjusted regression analyses for the modified VHS and two sub-scales. For the adjusted models, the Hosmer-Lemeshow test was used to determine the overall fit of the multivariable models (Hosmer et al., 1997). Additionally, the Variance Inflation Factor (VIF) was used to assess for multicollinearity between the independent variables included in the final adjusted models. A VIF less than 5 was considered acceptable (James et al., 2021; Lewis-Beck & Lewis-Beck, 2016). Given that the VHS is a Likert scale and there is an argument that it could be treated as a continuous variable, the multivariable analysis was repeated using linear regression analysis to determine if there were any major differences in the predictors of vaccine hesitancy when compared with the effect of dichotomizing the VHS. 3.8.2.3 Vaccine Hesitancy Scale and Vaccine Intentions Logistic regression was also used to examine the relationship between vaccine hesitancy, based on the findings of the modified VHS, and a parent’s intention to vaccinate their child with a COVID-19 vaccine if approved for use in children under 12 years of age. The outcome variable for this analysis was vaccine intention. Vaccine hesitancy was included as an independent variable. Predictors of vaccine hesitancy from the Vaccine Hesitancy Matrix were also included 51 in this analysis as covariates to determine their impact on the relationship between parent hesitancy and vaccine intentions. The same process was followed for this analysis, where an unadjusted logistic regression was run between each independent variable and the dependent variable. Independent variables with a p-value < .2 were included in the multivariable model (Hosmer et al., 2013). For the multivariable model, a p-value of < .05 was considered statistically significant. A goodness-of-fit test was completed and the VIF was applied to assess for multicollinearity (Hosmer et al., 1997; James et al., 2021; Lewis-Beck & Lewis-Beck, 2016). 3.9 Ethical Considerations Survey data was collected on a secure UBC-licensed Qualtrics account. All participant information was anonymized, and no participant names or email addresses were accessible during data analysis to uphold the anonymity of participants. The study has been approved by the UBCO Behavioural Research Ethics Board. Despite using data from a larger survey, data analysis was not previously completed on the sub-sample of parents included in this study and the objectives of this study were within in the overall objectives of the larger study. For those reason, and after consultation with the UBCO Behavioural Research Board ethics board office, this study was not considered a secondary analysis. The study fell into the minimal-risk category with medium participant vulnerability and low research risk. Minimal-risk research includes studies where “the probability and magnitude of possible harms implied by participation in the research are no greater than those encountered by participants in those aspects of their everyday life” (Canadian Institue of Health Research et al., 2022, p. 25). The survey fell into the category of minimal risk since the timeline of the survey occurred when conversations about COVID-19 and pediatric vaccines were highly publicized in the province of B.C. (De Faye et al., 2022). The risk of engaging in the questions in the survey were therefore similar to the risks of engaging in 52 conversations about global and local news. At the end of the survey, participants were provided resources related to COVID-19 and were encouraged to contact a physician or mental health professional as needed. Consent was obtained at the beginning of the survey with participants electronically selecting to agree or disagree to participate in the larger study. Participants were not able to access the survey content until consent was obtained. Participants were also allowed to download the consent form or provide an email to be contacted if they required more information before giving consent. If participants chose to disagree, they were unable to advance to the survey questions. 3.9.1 Risk Mitigation The data for this thesis had previously been collected. There was therefore no risk in data collection for this study. The responses from the survey were stored on a password protected UBC OneDrive. The responses from the survey will be deleted five years following publication. For this thesis, only data on the sub-sample of parents with children under 12 was accessible. Since this study used an online survey, risk mitigation strategies for survey fraud were considered (Lawlor et al., 2021). The risk of survey fraud was lower for this study given that participation in the study was not incentivised. Survey meta data, such as an Internet Protocol (IP) address can also be used to mitigate risk of survey fraud (Lawlor et al., 2021). During data cleaning, the IP address of each participant was checked for duplicates. For participants with the same IP address, the second version of the survey was deleted. 53 Chapter 4: Results In this chapter I present the results of the study. The inclusion and exclusion of participants and characteristics of the final sample of parents are described. The prevalence of vaccine hesitancy among the sample of parents and the results of the logistic regression analyses are presented. 4.1 Participant Inclusion and Exclusion For the larger study, 2110 individuals accessed the survey between October and November 2021. The exclusion of participants for both the larger study and current study are depicted in Figure 3. Six individuals declined to participate in the study after accessing the survey link. Additionally, 19 respondents were removed due to risk of fraud and 421 respondents were removed for incomplete demographic sections of the survey. The final sample of participants for the larger study includes 1667 respondents, 1018 of which self-identified as a parent to one or more child under the age of 12. From the sample of parents, one participant was excluded for living outside B.C. based on their response to city of residence in the survey and two participants were excluded for having entire survey sections incomplete for a child. Additionally, 22 participants were removed that had greater than two missing items from the nine included VHS items in the analysis. Parents missing one (n=32) or two (n=10) items on the VHS remained included in the study and the missing values were replaced using personal mean substitution methods (Downey & King, 1998; Hawthorne & Elliot, 2005). The substitution of missing data for 4.2% of participant responses falls far below the threshold of a 20% margin recommended to maintain accurate representation of an original dataset (Downey & King, 1998). After applying all exclusions, 993 parents living in B.C. with one or more child under the age of 12 years were included in the final analysis for this study. 54 Figure 3 Participant Flow Diagram 4.2 Participant Characteristics The demographic characteristics of the study participants are presented in Table 5. The table includes data from the 2021 B.C. census profile to allow for comparison between participant characteristics and provincial demographic characteristics (Statistics Canada, 2023). The number of children under 12 in a household is not reported in census data (Statistics Canada, 2023). Most participants were female (94%), age 30 to 39 years (64.8%), White (79%), with a bachelor’s degree or higher (45.4%) and an annual household income of $90,000 to $149,999 (43.61%). The majority of participants had one (41.5%) or two (42.6%) children. Table 6 displays health and vaccine related characteristics. Few children tested positive for COVID-19 (6.1%), were immunized for influenza (31.2%) or were diagnosed with a chronic illness (15%). The majority of participants were vaccinated for COVID-19 (62.54%), but less than half (43.6%) intended to vaccinate their child with a COVID-19 vaccine once approved for use in children.55 Table 5 Demographic Characteristics (N = 993) Characteristics Participants n(%) Census Characteristics B.C. Census Data* % Sex of Parent Gender (population 15 to 64 years) Female 933 (95%) Women 50.6% Male 42 (4.2%) Men 49.4% Non-Binary 5 (0.5%) Age of Parent (years) Age (years) < 30 145 (14.6%) 15 to 29 17.7% 30 to 39 643 (64.8%) 30 to 39 14.3% 40 + 205 (20.6%) 40 to 64 33.4% Annual Household Income Annual Household Income < $60,000 163 (16.4%) < $60,000 33.3% $60,000 – $89,999 168 (16.9%) $60,000 – $89,999 19.3% $90,000 – $149,999 433 (43.6%) $90,000 – $149,999 25.8% > $150,000 195 (19.6%) > $150,000 21.4% Highest Educational Attainment Highest Certificate, Diploma or Degree Grade 12 or Less 165 (16.6%) Grade 12 or Less 43% Certificate or College Diploma 377 (38%) Certificate or College Diploma 28.5% Bachelor’s Degree 290 (29.2%) Bachelor’s Degree 18.5% Postgraduate Degree or More 161 (16.2%) Postgraduate Degree or More 10.1% Race Visible Minority White 794 (80%) Not a visible minority 65.6% Asian 16 (1.6%) Visible Minority 34.4% Indigenous 23 (2.3%) Indigenous 5.9% Bi/Multi-Racial 86 (8.7%) Multiple Visible Minorities 1.2% Health Authority Health Authority Interior Health 330 (36.8%) Interior Health 15.8% Fraser Health 237 (26.4%) Fraser Health 37.7% Vancouver Costal Health 83 (9.3%) Vancouver Costal Health 24% Vancouver Island Health 139 (15.5%) Vancouver Island Health 16.7% Northern Health 108 (12%) Northern Health 5.8% Number of Children Under 12 1 413 (41.5%) 2 423 (42.6%) 3 125 (12.6%) 4 or more 32 (3.2%) * Census data retrieved from 2021 Census of Population (Interior Health, n.d.; Statistics Canada, 2023) 56 Table 6 Health and Vaccination Characteristics (N=993) Characteristics Participants n (%) Child Received Influenza Vaccine in the Last 12 Months Yes 310 (31.2%) Child Diagnosed with Chronic Illness Yes 149 (15%) Child Tested Positive for COVID-19 Yes 60 (6.1%) Unsure 24 (2.4%) Parent Tested Positive for COVID-19 Yes 75 (7.6%) Parent Received a COVID-19 Vaccine Yes 621 (62.5%) HCP Recommended Pediatric COVID-19 Vaccine Yes 109 (11%) Parent Intention to Vaccinate Child for COVID-19 Yes 433 (43.6%) 4.2.1 Reasons for Vaccine Intention Figure 4 and Figure 5 display parents’ reasons for their willingness or unwillingness to accept a COVID-19 vaccine for their child. The most common reasons for a parent’s willingness to accept a pediatric COVID-19 vaccine included concerns for a child falling ill from a COVID-19 infection, protecting others around the child, preventing the child from contracting a COVID-19 infection, and a belief that COVID-19 can be a serious illness in children. The most common reasons for a parent’s unwillingness to accept a pediatric COVID-19 vaccine for their child included concerns for possible side effects from the COVID-19 vaccine, a lack of information about the COVID-19 vaccine, a concern that the COVID-19 vaccine was developed too quickly, and a belief that a COVID-19 infection is not a serious illness in children. 57 Figure 4 Reasons for Willingness to Accept a COVID-19 Vaccine for a Child (N=993) 58 Figure 5 Reasons for Unwillingness to Accept a COVID-19 Vaccine for a Child (N=993) 59 4.3 Prevalence of Vaccine Hesitancy Participant responses to each of the nine items included in the modified VHS are displayed in Figure 6. For item 1 through item 6 on the modified VHS, which comprise the lack of confidence sub-scale, there were polarizing opinions across the sample of participants, with a relatively even number of participants in agreement (48.3% to 52.4%) or disagreement (47.6% to 52.4%) with each statement included in the scale. The polarization was best demonstrated with item 4 on the scale, where there was a 0.8% difference in the percentage of participants who agreed or strongly agreed (49.9%) and those participants who disagreed or strongly disagreed (49.1%) with the statement “Having my child vaccinated against COVID-19 will be important for the health of others in my community”. For the last three items on the modified VHS, comprising the risk sub-scale, there was a consistent majority of participants in agreement with the statements. For example, 61.9% of participants agreed with item 7, “The COVID-19 vaccine has not been around long enough to be sure that it is safe for children”, 68.9% of participants agreed with item 8, “I am concerned about the serious side effects of a COVID-19 vaccine for my child”, and 54.9% of participants agreed with item 9, “I think the COVID-19 vaccine might cause lasting health problems for my child.” It is important to consider that item 7, item 8, and item 9 on the modified VHS were negatively worded, meaning that a response in agreement with these statements represented a more negative outlook on vaccination.60 Figure 6 Response for Individual VHS Items (N=993) 61 In alignment with previous work on the validation of the WHO VHS, the nine items on the modified VHS scale were scored 1, 2, 4, and 5 so that a higher score always represented more negative beliefs towards vaccination (Helmkamp et al., 2021). To determine the mean VHS score, the cumulative score of all included items was divided by the total number of included items. This was completed for the nine items that comprised the total scale, the six items that comprised the lack of confidence sub-scale, and the three items that comprised the risk sub-scale. The mean scores for the total scale, lack of confidence sub-scale, and risk sub-scale are displayed in Table 7. The cut-off point of 3 was used to define hesitancy, with a mean score greater than 3 representing vaccine hesitancy for the total scale (Helmkamp et al., 2021). After applying the cut-off, 52.1% of the study participants were considered vaccine hesitant for pediatric COVID-19 vaccines based on the total mean score calculated for the 9-item VHS. The cut-off point of 3 was also used for the lack of confidence and risk sub-scales. For the lack of confidence sub-scale, 49.2% of participants had total mean VHS score greater than 3, demonstrating decreased confidence in pediatric COVID-19 vaccines. When comparing the total scale, lack of confidence sub-scale, and risk sub-scale, the mean hesitancy score is highest for the risk sub-scale. When the cut-off point of 3 was applied to the risk sub-scale, 61.2% of participants had a total mean VHS score greater than 3 indicated increased risk perception towards the COVID-19 vaccine. Figure 7 displays the mean score for each of the nine items included in the modified VHS to help illustrate the increase in mean scores for the risk sub-scale items. There is a noticeable increase in the mean score for item 7, item 8, and item 9, with mean scores 3.5, 3.7, and 3.2 respectively. Item 7, item 8, and item 9 comprise the risk sub-scale and include statements on the novelty of the vaccine, side effects, and long-term adverse effects of the vaccine. The mean score is highest for item 8, “The COVID-19 vaccine has not been around long enough to be sure that is safe for 62 children.” This demonstrates that participant’s perceived risks from pediatric COVID-19 vaccines were contributing to higher VHS scores. 63 Table 7 Parents of Children Under 12 Total Mean VHS Scores VHS Mean VHS Score Mean (SD) Mean VHS Score > 3 n (%) Mean VHS Score < 3 n (%) 9-Item Scale 3.18 (1.61) 517 (52.1%) 476 (47.9%) 6-Item Lack of Confidence Sub-Scale 3.02 (1.73) 489 (49.2%) 504 (50.8%) 3-Item Risk Sub-Scale 3.48 (1.52) 608 (61.2%) 385 (38.8%) 64 Figure 7 Parents of Children Under 12 Mean Score Per VHS Item (N=993) 65 4.4 Predictors of Vaccine Hesitancy The results of the unadjusted analysis between predictive factors and vaccine hesitancy using the total scale, lack of confidence sub-scale, and risk sub-scale are displayed in Table 8. For outcomes based on the 9-item modified VHS and the confidence sub-scale, all included independent variables demonstrated statistical significance (p < .2) in the unadjusted logistic regression analysis. Although some individual items within the categorical variables were not significant (i.e., income bracket $60,000 – $89,999 for the modified VHS), the entire variable was still included in the multivariable model to avoid re-categorizing the original variable (Hosmer et al., 2013). For outcomes based on the risk sub-scale, the variable participant age was not statistically significant (p > .2) in the unadjusted logistic regression analysis and was therefore excluded from the adjusted multivariable model. The results of the adjusted analysis are displayed in Table 9. Significant findings are relatively consistent across the three multivariable models for the total scale, lack of confidence sub-scale, and risk sub-scale. Most contextual factors from the Vaccine Hesitancy Matrix included in the models did not demonstrate statistical significance in the adjusted models. The variable number of children under 12 in the family was found to be statistically significant across all three models. For the total scale, for every additional child under 12 in the household participants were 1.66 times more likely to be vaccine hesitant (aOR = 1.66, 95% CI: 1.26 – 2.19). For the lack of confidence sub-scale, for every additional child under 12 in the household participants were 1.73 time more likely to have a significant decrease in vaccine confidence (aOR = 1.73, 95% CI: 1.3 – 2.31). For the risk sub-scale, for every additional child under 12 in the household participants were 1.37 times more likely to have significant increase in risk perception for pediatric COVID-19 vaccines (aOR = 1.37, 95% CI: 1.08 – 1.74). The variable 66 participant age was only found to be statistically significant in the lack of confidence sub-scale model, with parents aged 40 years or older being 2.26 times more likely to have a significant decrease in vaccine confidence in comparison to parents 30 years of age or younger (aOR = 2.26, 95% CI: 1.13 – 4.5). The variable education was found to be statistically significant for both the lack of confidence and risk sub-scale models. For the lack of confidence sub-scale model, parents with a post-graduate degree or higher were 57% less likely to have decreased vaccine confidence when compared to parents with a grade 12 or less education (aOR = 0.43, 95% CI: 0.19 – 0.97). For the risk sub-scale, parents with a certificate or college diploma were 2.48 times more likely to have increased risk perception for pediatric COVID-19 vaccines compared to parents with a grade 12 education or less (aOR = 2.48, 95% CI: 1.5 – 4.1). For the individual influential factors included in the adjusted analysis from the Vaccine Hesitancy Matrix, the variable parent COVID-19 vaccination status was found to be statistically significant in all three models. When compared with parents who were not vaccinated, parents who were vaccinated for COVID-19 were 99% less likely to be vaccine hesitant for pediatric COVID-19 vaccines (aOR = 0.01, 95% CI: 0.004 – 0.02), 98% less likely to have decreased vaccine confidence for pediatric COVID-19 vaccines (aOR = 0.02, 95% CI: 0.01 – 0.03), and 96% less likely to have increased risk perception for pediatric COVID-19 vaccines (aOR = 0.04, 95% CI: 0.02 – 0.07). Similarly, the variable child influenza immunization status in the last 12 months was also found to be statistically significant in all three models. In comparison to parents who did not immunize their children for influenza, parents who had vaccinated at least one child under the age of 12 for influenza in their family within the last 12 months were 82% less likely to be vaccine hesitant for pediatric COVID-19 vaccines (aOR = 0.18, 95% CI: 0.11 – 0.29), 87% less likely to have decreased vaccine confidence for pediatric COVID-19 vaccines (aOR = 0.13, 95% CI: 0.07 – 67 0.21), and 69% less likely to have increased risk perception for pediatric COVID-19 vaccines (aOR = 0.31, 95% CI: 0.23 – 0.44). The variable child health status was only found to be statistically significant in the risk sub-scale model, with parents who has at least one child under the age of 12 diagnosed with a chronic health condition being 49% less likely to have increased risk perception towards pediatric COVID-19 vaccines compared to parents who did not have a child with a chronic illness (aOR = 0.51, 95% CI: 0.31 – 0.84). The vaccine-specific factor from the Vaccine Hesitancy Matrix, HCP recommendations of the pediatric COVID-19 vaccine, was found to be statistically significant across all three models. When compared with participants who did not receive HCP recommendation, participants who received a recommendation from their HCP to vaccinate their child with a COVID-19 vaccine were 59% less likely to be vaccine hesitant for pediatric COVID-19 vaccines (aOR = 0.41, 95% CI: 0.2 – 0.84), 59% less likely to have decreased vaccine confidence for pediatric COVID-19 vaccines (aOR = 0.41, 95% CI: 0.19 – 0.89), and 69% less likely to have increased risk perception for pediatric COVID-19 vaccines (aOR = 0.56, 95% CI: 0.34 – 0.95). The result of the Hosmer-Lemeshow goodness-of-fit test for the three adjusted multivariable models ranged from 0.42 to 0.62, respectively, indicating the models were a good fit for the data. VIF values ranged from 1.03 to 2.12 across the three adjusted models, demonstrating there were no concerns for multicollinearity between the independent variables. A linear regression analysis treating the results of the VHS as a continuous variable was completed to determine if there were major differences in findings compared to treating the VHS as a dichotomous variable. Of note, the results of the VHS did not follow a normal distribution and followed a parabolic distribution, further supporting the treatment of the VHS results as a dichotomous variable. After running a linear regression with mean vaccine hesitancy scores as a continuous variable, independent variables certificate or 68 college diploma and child health status under the total VHS model were found to be statistically significant. No additional differences were noted for the unadjusted or adjusted models for the 9-item VHS, the lack of confidence sub-scale, and the risk sub-scale when treating VHS results as a continuous variable. 69 Table 8 Unadjusted Associations Between Matrix Factors and Vaccine Hesitancy (N=993) Total Scale Lack of Confidence Sub-Scale Risk Sub-Scale Independent Variable N OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value Education Grade 12 or Less 165 1 - - - - 1 - - - - 1 - - - - Certificate or College Diploma 377 1.31 0.9 – 1.9 .16 1.23 0.85 – 1.78 .27 1.86 1.26 – 2.75 .002 Bachelor’s Degree 290 0.62 0.42 – 0.9 .01 0.6 0.41 – 0.88 .009 0.73 0.49 – 1.08 .11 Postgraduate Degree or More 161 0.38 0.24 – 0.6 < .001 0.35 0.22 – 0.55 < .001 0.43 0.28 – 0.67 < .001 Income Less than $60,000 163 1 - - - - 1 - - - - 1 - - - - $60,000 - $89,000 168 0.8 0.52 – 1.24 .32 0.79 0.51 – 1.22 .28 0.68 0.43 – 1.07 .1 $90,000 - $149,000 433 0.64 0.44 – 0.92 .02 0.64 0.45 – 0.93 .02 0.64 0.44 – 0.95 .03 $150,000 or Greater 195 0.49 0.32 – 0.75 .001 0.45 0.29 – 0.68 < .001 0.44 0.28 – 0.68 < .001 Not Reported 34 2.1 0.9 – 4.93 .09 2.33 0.99 – 5.45 .05 1.36 0.57 – 3.21 .49 Parent Age Less than 30 years 145 1 - - - - 1.00 - - - - 1 - - - - 30 – 39 years 643 1.39 0.97 – 2 .08 1.3 0.9 – 1.88 .16 1.04 0.72 – 1.51 .82 40 years or older 205 1.91 1.24 – 2.94 .003 2.04 1.32 – 3.14 .001 1.29 0.83 – 2.01 .25 Number of Children Under 12 (Continuous) 993 1.39 1.18 – 1.64 < .001 1.4 1.19 – 1.65 < .001 1.23 1.1 – 1.54 .002 Parent Vaccinated for COVID-19 No 372 1 - - - - 1 - - - - 1 - - - - Yes 621 0.01 0.002 – 0.01 < .001 0.02 0.009 – 0.02 < .001 0.02 0.01 – 0.04 < .001 Child Influenza Vaccine in the Last Year No 683 1 - - - - 1 - - - - 1 - - - - Yes 310 0.07 0.05 – 0.1 < .001 0.05 0.03 – 0.08 < .001 0.12 0.09 – 0.16 < .001 Child Diagnosed with Chronic Illness No 844 1 - - - - 1 - - - - 1 - - - - Yes 149 0.69 0.49 – 0.98 .04 0.72 0.51 – 1.02 .07 0.59 0.42 – 0.84 .003 HCP RecommendedCOVID-19 Vaccine No 884 1 - - - - 1 - - - - 1 - - - - Yes 109 0.34 0.22 – 0.53 < .001 0.35 0.23 – 0.55 < .001 0.37 0.24 – 0.55 < .001 70 Table 9 Adjusted Associations Between Matrix Factors and Vaccine Hesitancy (N=993) Total Scale Lack of Confidence Sub-Scale Risk Sub-Scale Independent Variable N OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value Education Grade 12 or Less 165 1 - - - - 1 - - - - 1 - - - - Certificate or College Diploma 377 1.49 0.83 – 2.65 .18 1.33 0.74 – 2.39 .34 2.48 1.5 – 4.1 .001 Bachelor’s Degree 290 0.8 0.43 – 1.5 .49 0.77 0.41 – 1.45 .41 1.10 0.67 – 1.85 .71 Postgraduate Degree or More 161 0.54 0.25 – 1.17 .12 0.43 0.19 – 0.97 .04 0.74 0.4 – 1.38 .34 Income Less than $60,000 163 1 - - - - 1 - - - - 1 - - - - $60,000 - $89,000 168 1.06 0.54 – 2.08 .86 1.1 0.55 – 2.18 .8 0.69 0.38 – 1.23 .22 $90,000 - $149,000 433 0.94 0.53 – 1.7 .85 1.02 0.56 – 1.85 .95 0.86 0.52 – 1.42 .55 $150,000 or Greater 195 1.07 0.53 – 2.16 .9 0.89 0.43 – 1.86 .76 0.78 0.43 – 1.42 .42 Not Reported 34 1.57 0.44 – 5.58 .5 2 0.56 – 7.12 .29 0.74 0.24 – 2.28 .6 Parent Age Less than 30 years 145 1 - - - - 1.00 - - - - 1 - - - - 30 – 39 years 643 1.39 0.8 – 2.42 .24 1.17 0.66 – 2.06 .54 40 years or older 205 1.93 0.98 – 3.8 .06 2.26 1.13 – 4.5 .02 Number of Children Under 12 (Continuous) 993 1.66 1.26 – 2.19 < .001 1.73 1.3 – 2.31 < .001 1.37 1.08 – 1.74 .008 Parent Vaccinated for COVID-19 No 372 1 - - - - 1 - - - - 1 - - - - Yes 621 0.01 0.004 – 0.02 < .001 0.02 0.01 – 0.03 < .001 0.04 0.02 – 0.07 < .001 Child Influenza Vaccine in the Last Year No 683 1 - - - - 1 - - - - 1 - - - - Yes 310 0.18 0.11 – 0.29 < .001 0.13 0.07 – 0.21 < .001 0.31 0.23 – 0.44 < .001 Child Diagnosed with Chronic Illness No 844 1 - - - - 1 - - - - 1 - - - - Yes 149 0.61 0.33 – 1.12 .1 0.64 0.35 – 1.2 .17 0.51 0.31 – 0.84 .007 HCP RecommendedCOVID-19 Vaccine No 884 1 - - - - 1 - - - - 1 - - - - Yes 109 0.41 0.2 – 0.84 .02 0.41 0.19 – 0.89 .02 0.56 0.34 – 0.95 .03 71 4.5 Vaccine Hesitancy and Vaccine Intentions The results of the unadjusted and adjusted analysis between vaccine hesitancy and vaccine intentions are displayed in Table 10. In the unadjusted logistic regression analysis, the variable vaccine hesitancy was found to be strongly associated with vaccine intentions. A vaccine hesitant parent, defined by a mean score greater than 3 on the 9-item modified VHS, was significantly less likely to intend to vaccinate their child when compared with a parent who was not vaccine hesitant (aOR = 0.0009, 95% CI: 0.0003 – 0.002). All covariates included in the unadjusted model demonstrated significance (p < .2) and were included in the adjusted logistic regression model. In the adjusted model, the relationship between vaccine hesitancy and vaccine intentions remained strong despite the inclusion of covariates. In the adjusted model, parents who were vaccine hesitant were found to have significantly decreased intentions to vaccinate their child with a pediatric COVID-19 vaccine when compared with parents who were not vaccine hesitant (aOR = 0.002, 95% CI: 0.0006 – 0.006). The results of the Hosmer-Lemeshow goodness-of-fit test was 0.92 and VIF values ranged 1.03 to 2.36 for the adjusted model, indicating the model was a good fit for the data and the assumptions for multicollinearity were met.72 Table 10 Association Between Vaccine Hesitancy and Intentions with Covariates (N=993) Independent Variables Unadjusted Analysis Adjusted Analysis N OR 95% CI p-Value OR 95% CI p-Value Education Grade 12 or Less 165 1 - - - - 1 - - - - Certificate or College Diploma 377 0.63 0.43 – 0.93 .019 0.3 0.11 – 0.81 .02 Bachelor’s Degree 290 1.55 1.05 – 2.28 .027 0.76 0.26 – 2.23 .62 Postgraduate Degree or More 161 2.6 1.66 – 4.06 < .001 1.25 0.34 – 4.52 .74 Income Less than $60,000 163 1 - - - - 1 - - - - $60,000 - $89,000 168 1.2 0.77 – 1.86 .43 0.92 0.28 – 3.09 .9 $90,000 - $149,000 433 1.38 0.95 – 2 .09 0.55 0.2 – 1.56 .26 $150,000 or Greater 195 1.92 1.26 – 2.94 .003 0.69 0.21 – 2.31 .55 Not Reported 34 0.53 0.22 – 1.24 .14 2 0.11 – 36.4 .64 Age of Parent Less than 30 years 145 1 - - - - 1.00 - - - - 30 – 39 years 643 0.87 0.61 – 1.25 .46 1.82 0.87 – 3.81 .11 40 years or older 205 0.7 0.46 – 1.08 .11 3.23 1.03 – 10.11 .04 Number of Children Under 12 (Continuous) 993 0.76 0.64 – 0.89 .001 0.91 0.59 – 1.41 .68 Parent Received a COVID-19 Vaccine No 372 1 - - - - 1 - - - - Yes 621 205.56 75.63 – 558.71 < .001 8.64 2.07 – 36.02 .003 Child Received Influenza Vaccine in the Last 12 Months No 683 1 - - - - 1 - - - - Yes 310 12.77 9.13 – 17.87 < .001 2.44 1.27 – 4.71 .008 Child Diagnosed with Chronic Illness No 844 1 - - - - 1 - - - - Yes 149 1.66 1.17 – 2.36 .004 3.15 1.06 – 9.39 0.04 HCP Recommended Pediatric COVID-19 Vaccine No 884 1 - - - - 1 - - - - Yes 109 2.82 1.86 – 4.28 < .001 0.87 0.33 – 2.32 .79 Results of VHS Not Hesitant (mean VHS score < 3) 476 1 - - - - 1 - - - - Hesitant (mean VHS score > 3) 517 0.0009 0.0003 – 0.002 <.001 0.002 0.0006 – 0.006 < .001 73 Chapter 5: Conclusion In this chapter I relate the findings of this study to the global body of research on pediatric COVID-19 vaccine hesitancy. Potential contributions from study findings on clinical practice are presented. Study limitations, future research recommendations, and a personal reflection on the research process are then discussed. 5.1 Discussion At the start of the pandemic, vaccine hesitancy was identified as an expected barrier for pediatric COVID-19 vaccine promotion and uptake (Dror et al., 2020). This study shows that vaccine hesitancy for pediatric COVID-19 vaccines was prevalent among B.C. parents. Parents were most concerned with the safety of the novel pediatric COVID-19 vaccine. Predictive factors of vaccine hesitancy remained relatively consistent between the total VHS, lack of confidence sub-scale, and risk sub-scale, revealing strong influential factors on parental vaccine decision-making. This study also confirmed a strong relationship between hesitancy and intentions, indicating application of the VHS in clinical practice could help predict vaccination behaviors. Although it is difficult to pinpoint a root cause of low vaccine uptake, given the fluidity and complexity of the vaccine decision-making process, study findings suggest that vaccine hesitancy toward the pediatric COVID-19 vaccine was prevalent among B.C. parents and likely underpinning their vaccine decisions (Larson et al., 2014). The study modified the VHS and applied a mid-point cut-off, based on previous work modifying the WHO VHS, to determine the prevalence of vaccine hesitancy among the study participants (Helmkamp et al., 2021). The results showed that over one-half of the participants in the study were considered vaccine hesitant. Aedh (2022), Al-Qahtani et al. (2022), and Delgado-Gallegos et al. (2021) reported overall prevalence of vaccine hesitancy between 55.9% to 72% 74 among their samples, making the study findings comparable to other global research. It was not surprising that the prevalence of vaccine hesitancy in the current study was high, given the low pediatric COVID-19 vaccine rates in B.C. In fact, it was expected that the prevalence of vaccine hesitancy in the current study would have been higher than 52.1%, since 55% of children aged 5 to 11 and 81.3% of children aged 0 to 4 in B.C. were not vaccinated with a single dose of a COVID-19 vaccine (Government of Canada, 2024). In another sample of Canadian parents, Humble et al. (2022) also reported a difference in intention among their sample versus actual vaccination rates, with 56% of their sample intending to vaccinate their children between the ages of 5 to 11 years old compared to 37% of children in Canada who were vaccinated with two doses of the COVID-19 vaccine four months following its approval. The exact reason for lower than expected vaccine uptake is unclear, however Humble et al. (2022) report it could be related to parent concerns regarding vaccine safety in younger children. The difference could also be related to the dynamic nature of the pandemic with evolving variants of concern between the surveying period and the time when pediatric vaccines were widely available in B.C. The surveying period for the study occurred during the peak of the highly transmissible Delta variant, a period of increased disease severity and public fear (BCCDC, 2021; De Faye et al., 2022). This may have contributed to study participants having more positive outlooks on pediatric COVID-19 vaccination during the surveying period with greater intentions to protect their children from severe infection through vaccination. The Delta wave was a much different pandemic state compared to the Omicron wave, which occurred into spring and summer 2022 when pediatric vaccines were being approved and heavily promoted (De Faye et al., 2022). Both provincial and federal agencies reported that children infected with the Omicron variant of COVID-19 experienced very mild illness such as nasal congestion, sneezing, or a sore throat and it was very 75 rare for children to require hospitalization (BCCDC, 2023a; Government of Canada, 2022). This, in addition to decreased illness severity in adult populations, relaxed public health restrictions, and high adult vaccination coverage during the Omicron wave of the pandemic may have contributed to parents experiencing less urgency for vaccine uptake in comparison to previous perceptions during the Delta wave of the pandemic (De Faye et al., 2022; Government of British Columbia, 2022). The modification and application of the VHS in this study allowed for a better understanding of which factors within the scale most contributed to participant experiences of hesitancy. The results showed that participants were most in agreement for the statements “The COVID-19 vaccine has not been around long enough to be sure that it is safe for children”, “I am concerned about serious side effects of a COVID-19 vaccine for my child”, and “I think the COVID-19 vaccine might cause lasting health problems for my child.” These three statements comprise the risk sub-scale, which pertain to risk-benefit decision-making and focus heavily on safety (Helmkamp et al., 2021; Peretti-Watel et al., 2015; Shapiro et al., 2018). Over two-thirds of parents included in this study either strongly agreed or agreed with that statement “I am concerned about serious side effects of a COVID-19 vaccine for my child.” Martinez et al. (2022) and Aedh (2022) reported that 51.9% and 79.3% of participants either agreed or strongly agreed with the same VHS statement in their studies, respectively. Other authors have also identified side effects as a significant concern for parents in their studies (Skeens et al., 2022a; Skeens et al., 2022b; Szilagyi et al., 2021). The results of the current study indicated that vaccine safety was important to participants. The challenge of overcoming safety concerns for novel vaccines was not new. A decade prior to COVID-19, B.C. parents previously expressed that safety and concerns for side effects will substantially contribute to their decision-making during 76 pandemics requiring the introduction of novel vaccines (Henrich & Holmes, 2009). The findings from this study support that even amidst a public health crisis, vaccine safety remained a consistent concern for parents that likely contributed to them experiencing vaccine hesitancy. The study used regression analysis to understand vaccine hesitancy among parents in B.C. The results showed that various factors contributed to this hesitancy. The complexity of vaccine hesitancy was evident in all three analyzed models, with many factors showing statistical significance. This indicated a combination of influences were affecting vaccine hesitancy experiences. The study supported the Vaccine Hesitancy Matrix, showing that contextual, individual, and vaccine-related factors played a role in parent experiences of vaccine hesitancy. Some factors consistently stood out in all three models, such as the number of children under 12 in the household, the parent’s COVID-19 vaccination status, child influenza immunization status, and HCP vaccine recommendations. The number of children under 12 in the household was the only contextual factor from the Vaccine Hesitancy Matrix that significantly increased vaccine hesitancy, decreased vaccine confidence, and increased risk perceptions. This study found that the likelihood of vaccine hesitancy increased with larger families and is consistent with pre-pandemic studies on childhood immunizations (Chen et al., 2023; Danis et al., 2010). Chen et al. (2023) found that parents with three or four children were approximately 5% more likely to refuse vaccines than parents with a single child. Similarly, Danis et al. (2010) found that families with three or more children were significantly less likely to have fully vaccinated children compared with families with one child. The relationship between increased family size and decreased vaccine intentions has been attributed to competing household demands making vaccinating multiple children inconvenient (Chen et al., 2023; Danis et al., 2010). No additional contextual factors from the 77 Vaccine Hesitancy Matrix were consistently significant across the three multivariable models in the current study. This finding differs from other authors who have reported significant relationships between vaccine hesitancy and contextual factors such as age, income, or education (Aedh, 2022; He et al., 2022; Skeens et al., 2022a). However, there were inconsistencies between the findings of these studies. For example, Skeens et al. (2022a) reported that parents with a bachelors education were less vaccine hesitant, whereas Aedh (2022) reports education did not significantly impact vaccine hesitancy in their sample of parents. The differences between the findings of our current study and other international parent samples on relationships between contextual factors and vaccine hesitancy helps to illustrate the contextual nature of vaccine hesitancy. These differences also highlighted the challenge of using generalized assumptions about demographic groups to identify potential parent groups at higher risk for vaccine hesitancy. Although socioeconomic indicators are important to consider, other factors from the Vaccine Hesitancy Matrix such as past immunization status might be a more consistent indicator of vaccine hesitancy. For individual factors from the Vaccine Hesitancy Matrix, this study found that previous vaccine behaviors significantly predicted pediatric COVID-19 vaccine hesitancy, decreased vaccine confidence, and increased risk perception. Participants who reported they were vaccinated for COVID-19 or had immunized their child for influenza in the last twelve months were less likely to be vaccine hesitant than unvaccinated participants or participants with children unvaccinated for influenza. In another Canadian study, Humble et al. (2021) reported similar findings to the current study, stating that parents who previously immunized their child for influenza were 1.5 times more likely to vaccinate their child for COVID-19. Humble et al. (2021) also found that parents with low intentions to vaccinate themselves for COVID-19 were 78 less likely to vaccinate their child for COVID-19. The relationship between previous vaccine behaviors and pediatric COVID-19 vaccine hesitancy was well supported in studies with international samples of parents (Humble et al., 2021; Martinez et al., 2022; Szilagyi et al., 2021; Temsah et al., 2021). The relationship between COVID-19 vaccine hesitancy and previous immunization history highlights the importance of vaccine promotion regardless of a pandemic state. As we move forward from the COVID-19 pandemic, ongoing vaccine promotion for routine vaccines and annual influenza vaccines should continue to be prioritized to strengthen parental confidence and decrease perceived risks in vaccination to support vaccine uptake for future pandemics requiring the introduction of a novel vaccine. Finally, HCP recommendations, representing vaccine-specific factors from the Vaccine Hesitancy Matrix, were found to significantly decrease vaccine hesitancy, increase vaccine confidence, and decrease perceived risks in this study. Dube et al. (2018) reported that prior to the COVID-19 pandemic, HCP trust was strongly associated with positive vaccine intentions. In the current study, parents who directly received pediatric COVID-19 vaccine recommendations from their HCP were half as likely to be vaccine hesitant when compared with parents who did not receive HCP recommendations. A qualitative study with Canadian parents also identified HCP recommendations as a facilitator of pediatric COVID-19 vaccination (Yeo et al., 2022). Similar findings have also been reported for adult COVID-19 vaccines, where individuals who received HCP recommendations were more likely to be vaccinated with their first dose of the COVID-19 vaccine (Nguyen et al., 2021). Despite our findings demonstrating a positive influence from HCP recommendations on vaccine hesitancy, only 11% of participants in this study reported having received a HCP recommendation to vaccinate their child for COVID-19. Canadian HCP’s have reported decreased confidence in vaccine promotion, a lack of knowledge 79 on COVID-19 vaccines, evolving vaccine recommendations, and a belief that it is not their professional role to discuss COVID-19 vaccination as reasons they were not engaging in vaccine promotion with their patients (Dubé et al., 2022). Our study findings highlighted the active role HCPs play in promoting vaccine confidence for a novel vaccine during a pandemic. In the future, HCP knowledge and confidence should be strengthened with tailored education or resources to increase provider vaccine promotion to support public health immunization campaigns (Dubé et al., 2022). 5.1.1 Clinical Significance The final analysis in this study looked at the relationship between vaccine hesitancy and vaccine intentions to understand the clinical importance of the VHS. If results from the VHS could predict vaccine behaviors, utilizing the scale in a clinical setting could be of great value in screening parents to strategically implement vaccine education. If a strong relationship exists between vaccine hesitancy and vaccine behaviors, limited clinical time could be used more efficiently to increase vaccine uptake by initiating vaccine promotion only for hesitant parents. In the current study, vaccine hesitant parents, defined by the modified VHS, were significantly less likely to intend to vaccinate their child for COVID-19 in comparison to parents who were not hesitant. This relationship between hesitancy and intentions demonstrated that the VHS could also be used to predict vaccine behaviors. Current literature supports this relationship, with Temsah et al. (2021) also reporting a statistically significant relationship between intentions and hesitancy among their sample of parents. Temsah et al. (2021) found that only 22.3% of parents who were vaccine hesitant, defined by a similarly modified version of the WHO VHS, intended to vaccinate their children with a COVID-19 vaccine. In the current study, the relationship between hesitancy and intentions remained strong even with the inclusion of multiple covariates 80 in the analysis. Given the strong relationship between intentions and hesitancy, some may argue why implementing vaccine promotion should be based on a parent completing a 9-item scale instead of simply asking a parent whether they intend to vaccinate their child. The original WHO VHS was developed after calls for a tool that facilitates understanding the underlying nature of vaccine hesitancy to better support public health responses (Larson et al., 2015). Inquiring solely about a parent’s intentions does not provide sufficient information to implement targeted vaccine education. Utilizing the VHS in clinical practice could allow HCPs to flag trends in particular areas of concern for parents to government agencies to facilitate more meaningful vaccine campaigns. Pre-pandemic, the WHO developed the Tailoring Immunization Programmes approach to support immunization program planning (WHO, 2019a). Although the technical documents supporting this approach are complex and intended for national campaigns, its fundamental principles could be applied in smaller settings (Habersaat et al., 2021). The first principle in the Tailoring Immunization Programmes approach is to diagnose the problem, or in other words, to understand the challenges or barriers to vaccine uptake (Habersaat et al., 2021; WHO, 2019a). Vaccine promotion is then targeted towards the root cause of suboptimal vaccine uptake (WHO, 2019a). At a much smaller scale, but in a similar manner to this approach, clinicians could apply the VHS to provide tailored vaccine education in clinical practice. Applying a modified version of the WHO VHS in a public health crisis could allow clinicians to understand individual items of concern on the scale or determine if parents lack confidence or have increased risk perception towards vaccines based on the two sub-scales included within the VHS. If a parent scored higher in the first six questions of the VHS that comprising the lack of confidence sub-scale, a clinician could build confidence by discussing the importance of herd immunity, the specific importance of the vaccine to a child’s health, or discussing the efficacy of 81 the vaccine. If a parent scored higher on the last three VHS items comprising the risk sub-scale, a discussion could focus more on the vaccine development process or clinical trials and the rarity of side effects. In turn, this could address the challenge of generic education approaches to a complex problem and support clinicians tailoring a parent’s vaccine education to their root cause of concern. 5.2 Strengths, Limitations and Future Recommendations To my knowledge, this was the first study that modified the WHO VHS to understand pediatric COVID-19 vaccine hesitancy in a B.C. sample of parents. This study provided an initial measurement of vaccine hesitancy during the Delta wave of the COVID-19 pandemic for future studies to compare the progression of vaccine hesitancy in the province of B.C. This study also had a large sample size with participants from all five regional health authorities in the province, providing significant insight into the highly contextual pandemic and challenges of vaccine hesitancy specific to B.C. Despite a lack of validation, the application of a modified WHO VHS and utilization of the scale as the primary outcome variable provided a more objective understanding of vaccine hesitancy and was appropriate given the emergence of a public health crisis. This study was not without its limitations. First, the timeline of the study, with the approval of pediatric COVID-19 vaccines in B.C. occurring in the middle of the survey period, may have impacted parent responses. As a result of this timeline and the cross-sectional design, this study was limited to measuring vaccine intentions consistently among all parents rather than actual vaccine uptake. Rates of vaccine uptake may have differed from what parents reported as their intentions. Second, an online survey with convenience sampling methods on a polarizing topic such as vaccine hesitancy could have led to selection bias. Pro-vaccine or anti-vaccine 82 groups could have skewed responses. However, there was a relatively even distribution of hesitant and not-hesitant parents in this sample similar to other international study findings (Aedh, 2022; Al-Qahtani et al., 2022; Delgado-Gallegos et al., 2021). Third, generalizability may have been affected by the sample of participants included this study. This study had a higher percentage of participants who identified as White, female, with higher household incomes, and with higher education attainments than the general B.C. population (Statistics Canada, 2023). Minority populations including Indigenous peoples and people of colour, lower income households, and people with lower education were underrepresented in this study. The underrepresentation of minority groups may have impacted study findings given that factors such as culture and socioeconomic status are known influential factors on vaccine hesitancy in the Vaccine Hesitancy Matrix (WHO, 2014). Most childhood vaccination studies have predominantly female participants, so this study was not unusual in that regard (Delgado-Gallegos et al., 2021; Durkin et al., 2023; He et al., 2022; Martinez et al., 2022). Future studies on vaccine hesitancy should attempt to have better representation of male participants and minority groups. Finally, participants were only asked to complete the VHS once in the survey despite answering other sections of the survey for up to four children. This means that a parent’s VHS score could not be examined for associations with a specific child. Parent’s hesitancy towards vaccines may have differed between children, depending on their age or individual health status. Future research should validate the modifications to the WHO VHS specific to pediatric COVID-19 vaccinations. Not only would this support continued use of the VHS to better understand pediatric COVID-19 vaccine hesitancy, but it would also support the modification of the scale for future pandemics that require an emergent understanding of potential barriers to the 83 uptake of a novel vaccine. In an ideal situation, a validated version of the VHS would exist where clinicians can easily exchange the name of a specific vaccine in each statement within the scale. Additionally, now that pediatric COVID-19 vaccines have been approved for use for a sufficient period of time, a similar study could be completed to understand how predictors of vaccine hesitancy have changed over the course of the pandemic and could determine if there is a relationship between vaccine hesitancy and actual pediatric COVID-19 vaccine uptake. This would strengthen the argument to implement a modified version of the VHS in clinical practice. Finally, the WHO definition of vaccine hesitancy should be reviewed. It was clear in this study that experiences of vaccine hesitancy varied between individuals and there was a very complex interplay of multiple factors contributing these experiences. Defining vaccine hesitancy as “the refusal of vaccines despite the availability of vaccination services” (WHO, 2014, p. 7) does not encompass the complexity of this public health threat. After applying the VHS in this study, the definition does not align with the Likert scale that clearly demonstrates vaccine hesitancy ranges on a continuum beyond simply vaccine refusal. The definition should be reviewed and revised to more clearly articulate the full scope of vaccine hesitancy. 5.3 Personal Reflection My experience throughout this graduate degree has strengthened my appreciation for the research process and exposed me to areas of nursing I would likely not have encountered in my professional career. Child health was not a topic that I thought would particularly resonate with me when I first began my studies, however I now have a much deeper appreciation for the work of researchers and clinicians who focus on pediatric vaccine promotion. Beyond specifics to pediatric vaccination, I have developed a great interest in the concept of vaccine hesitancy and would like to continue to participate in research around this topic in the future. Prior to beginning 84 this degree, I had a very basic understanding of vaccine hesitancy. Now, I find myself intrigued by the complexities of this public health crisis and the interconnectedness of the many factors that contribute to an individual’s hesitancy. I began this thesis without experience in conducting research, and after reflecting on this study, I am proud of the work I was able to accomplish and my growth in utilizing quantitative research methods and statistical analysis software. The skills in writing, critiquing evidence, and defending arguments that I have developed and strengthened throughout the last two years will go far beyond pediatric COVID-19 vaccines and will greatly benefit me as I advance my career into a nursing leadership role. I have successfully managed to balance working in clinical practice full-time and maintaining full-time enrollment in the Master of Science in Nursing program over the last two years. This experience has brought forward reflection on the work that needs to be done to create roles where individuals are given opportunities to advance nursing knowledge through graduate degrees while simultaneously continuing their nursing practice. With the nursing shortage and few nurses pursing graduate education, clinical and academic institutions should continue to work together to support balancing these opportunities for nursing. I hope to support the success of graduate students in the future who wish to do the same. Since completing the study, I now have a much clearer understanding of the research process and the importance of a clear study plan. I am very grateful to have been able to utilize previously collected survey data for this study. If I were to conduct this study again, I would apply the Vaccine Hesitancy Matrix from the beginning of the research process as a foundational theoretical framework. This may have strengthened my selection of independent variables and allowed for a more even representation of the three categories within the Vaccine Hesitancy Matrix. Additionally, within the survey section that included items from the Vaccine Confidence 85 Scale and VHS, I would have eliminated all questions except for the 9-items comprising the modified VHS included within the analysis. This decision would have decreased the length of the survey, preventing survey fatigue and hopefully allowing for more attentive responses to the items included in the analysis. Finally, now that we continue to see challenges with pediatric COVID-19 vaccine uptake two years after the survey was completed, there perhaps was a missed opportunity utilizing a cross-sectional design. It would be interesting to have used a follow-up study design to return to participants once pediatric vaccines were approved for use and throughout the pandemic. This would have allowed us to understand if parents who intended to vaccinate their child followed through with vaccine uptake and to understand the evolution of vaccine hesitancy throughout the waves of COVID-19. 5.4 Concluding Statement This thesis used a modified version of the WHO VHS to explore pediatric COVID-19 vaccine hesitancy among a sample of parents to children under 12 in B.C. Vaccine hesitancy was thought to be a substantial challenge to overcome during the promotion of a novel COVID-19 vaccine for children. The results of the study indicate that vaccine hesitancy was prevalent among B.C. parents, with greatest concerns pertaining to vaccine safety. Various factors, including number of children in the household, parent COVID-19 vaccine status, child influenza immunization status, and HCP vaccine recommendations significantly predicted pediatric COVID-19 vaccine hesitancy. This study also demonstrated a strong relationship between vaccine hesitancy and vaccine intentions, highlighting a possible avenue to utilize the VHS in clinical practice to predict vaccine intentions and strategically implement vaccine promotion resources. The findings from this study could contribute to the continued promotion of pediatric COVID-19 vaccines or be similarly applied for future pandemics in B.C. 86 Bibliography A, K., Lu, X., Wang, J., Hu, L., Li, B., & Lu, Y. (2021). Association between adult vaccine hesitancy and parental acceptance of childhood COVID-19 vaccines: A web-based survey in a Northwestern Region in China. Vaccines, 9(10), 1088. https://doi.org/10.3390/vaccines9101088 Aedh, A. I. (2022). Parents' attitudes, their acceptance of the COVID-19 vaccines for children and the contributing factors in Najran, Saudi Arabia: A cross-sectional survey. Vaccines, 10(8), 1264. https://doi.org/10.3390/vaccines10081264 Akel, K. B., Masters, N. B., Shih, S.-F., & Wagner, A. L. (2021). Modification of a vaccine scale for use in adult vaccinations in the United States and China. Human Vaccines & Immunotherapeutics, 17(8), 2639-2646. https://doi.org/10.1080/21645515.2021.1884476 Al-Amer, R., Maneze, D., Everett, B., Montayre, J., Villarosa, A. R., Dwekat, E., & Salamonson, Y. (2022). COVID-19 vaccination intention in the first year of the pandemic: A systematic review. Journal of Clinical Nursing, 31(1), 62-86. https://doi.org/10.1111/jocn.15951 Al-Qahtani, A. M., Mannasaheb, B. A., Shaikh, M. A. K., Alajlan, S. A., Alayed, M. S. Z., Shaikh, I. A., Asdaq, S. M. B., Al-Qahtani, F. S., Ghazwani, E. Y., Al-Qahtani, N. S., & Abbag, B. F. (2022). Parental willingness for COVID-19 vaccination among children aged 5 to 11 years in Riyadh City, Saudi Arabia: A cross-sectional study. Vaccines, 10(12), 1979. https://doi.org/10.3390/vaccines10121979 Aldakhil, H., Albedah, N., Alturaiki, N., Alajlan, R., & Abusalih, H. (2021). Vaccine hesitancy towards childhood immunizations as a predictor of mothers' intention to vaccinate their 87 children against COVID-19 in Saudi Arabia. Journal of Infection and Public Health, 14(10), 1497-1504. https://doi.org/10.1016/j.jiph.2021.08.028 Bannon Jr, W. (2015). Missing data within a quantitative research study: How to assess it, treat it, and why you should care. Journal of the American Association of Nurse Practitioners, 27(4), 230-232. https://doi.org/10.1002/2327-6924.12208 BC Centre for Disease Control. (2021). Weekly update on variants of concern (VOC). http://www.bccdc.ca/Health-Info-Site/Documents/VoC/VoC_Weekly_10152021.pdf BC Centre for Disease Control. (2023a, September 19). COVID-19 illness in children. Retrieved Feb 14, 2024 from http://www.bccdc.ca/health-info/diseases-conditions/covid-19/covid-19-and-children/covid-19-illness-in-children BC Centre for Disease Control. (2023b, July 6). COVID-19 vaccination coverage. Retrieved September 4, 2023 from http://www.bccdc.ca/health-professionals/data-reports/covid-19-vaccination-coverage BC Centre for Disease Control. (2024, March 9). Respiratory surveillance: COVID-19 situation report. Retrieved March 19, 2024 from https://bccdc.shinyapps.io/respiratory_covid_sitrep/ Bland, J. M., & Altman, D. G. (1997). Statistic notes: Cronbach’s alpha. BMJ, 314, 572. https://doi.org/10.1136/bmj.314.7080.572 Canadian Institutes of Health Research, Natural Sciences and Engingeering Research Council of Canada, & Social Sciences and Humanities Research Counicl of Canada. (2022). Tri-Council policy statement: Ethical counduct for research involving humans. https://ethics.gc.ca/eng/documents/tcps2-2022-en.pdf 88 Chen, F., He, Y., & Shi, Y. (2022). Parents’ and guardians’ willingness to vaccinate their children against COVID-19: A systematic review and meta-analysis. Vaccines, 10(2), 179. https://doi.org/10.3390/vaccines10020179 Chen, R., Guay, M., Glibert, N. L., Dube, E., Witteman, H. O., & Hakim, H. (2023). Determinants of parental vaccine hesitancy in Canada: Results from the 2017 Childhood National Immunization Coverage Survey. BMC Public Health, 23(1), 2327. https://doi.org/10.1186/s12889-023-17079-4 Chyung, S. Y. Y., Roberts, K., Swanson, L., & Hankinson, A. (2017). Evidence-based survey design: The use of a midpoint on the likert scale. Performance Improvement, 56(10), 15-23. https://doi.org/10.1002/pfi.21727 Danis, K., Georgakopoulou, T., Stavrou, T., Laggas, D., & Panagiotopoulos, T. (2010). Socioeconomic factors play a more important role in childhood vaccination coverage than parental perceptions: A cross-sectional study in Greece. Vaccine, 28(7), 1861 - 1869 https://doi.org/10.1016/j.vaccine.2009.11.078 De Faye, B., Perrin, D., & Trumpy, C. (2022). COVID-19 lessons learned review: Final report. Retrieved from https://www2.gov.bc.ca/assets/gov/public-safety-and-emergency-services/emergency-preparedness-response-recovery/embc/reports/covid-19_lessons_learned_report.pdf Delgado-Gallegos, J. L., Padilla-Rivas, G. R., Gastelum-Arias, L. J., Zuniga-Violante, E., Aviles-Rodriguez, G., Arellanos-Soto, D., Franco-Villareal, H., Garza-Trevino, E. N., Cosio-Leon, M. L. A., Romo-Cardenas, G. S., Ramos-Jimenez, J., Rivas-Estrilla, A. M., Moreno-Cuevas, J. E., & Islas, J. F. (2021). Parent's perspective towards child COVID-19 vaccination: An online cross-sectional study in Mexico. International Journal of 89 Environmental Research and Public Health, 19(1), 290. https://doi.org/10.3390/ijerph19010290 Downey, R. G., & King, C. V. (1998). Missing data in likert ratings: A comparison of replacement methods. The Journal of General Psychology, 125(2), 175-191. https://doi.org/10.1080/00221309809595542 Dror, A. A., Eisenbach, N., Tailber, S., Morozov, N. G., Mizarchi, M., Zigron, A., Srouji, S., & Sela, E. (2020). Vaccine hesitancy: The next challenge in the fight against COVID-19. European Journal of Epidemiology, 35(8), 775-779. https://doi.org/10.1007/s10654-020-00671-y Dube, E., Gagnon, D., Nickels, E., Jeram, S., & Schuster, M. (2014). Mapping vaccine hesitancy — Country-specific characteristics of a global phenomenon. Vaccine, 32(49), 6649-6654. https://doi.org/10.1016/j.vaccine.2014.09.039 Dube, E., Gagnon, D., Ouakki, M., Bettinger, J. A., Guay, M., Halperin, S., Wilson, K., Graham, J., Witteman, H. O., MacDonald, S., Fisher, W., Monnais, L., Tran, D., Gagneur, A., Guichon, J., Saini, V., Heffernan, J. M., Meyer, S., Driedger, S. M., . . . Canadian Immunization Research, N. (2016). Understanding vaccine hesitancy in Canada: Results of a consultation study by the Canadian Immunization Research Network. PLos ONE, 11(6), e0156118. https://doi.org/10.1371/journal.pone.0156118 Dube, E., Gagnon, D., Ouakki, M., Bettinger, J. A., Witteman, H. O., MacDonald, S., Fisher, W., Saini, V., Greyson, D., & On behalf of the Canadian Immunization Research Network. (2018). Measuring vaccine acceptance among Canadian parents: A survey of the Canadian Immunization Research Network. Vaccine, 36(4), 545-552. https://doi.org/10.1016/j.vaccine.2017.12.005 90 Dubé, E., Labbé, F., Malo, B., Manca, T., Aylsworth, L., Driedger, S. M., Graham, J., Greyson, D., MacDonald, N., Meyer, S. B., Leigh, J. P., Sadarangani, M., Wilson, S., & MacDonald, S. E. (2022). “I don’t think there’s a point for me to discuss it with my patients”: Exploring health care providers’ views and behaviours regarding COVID-19 vaccination. Human Vaccines & Immunotherapeutics, 18(5), 2088970. https://doi.org/10.1080/21645515.2022.2088970 Dube, E., Laberge, C., Guay, M., Bramadat, P., Roy, R., & Bettinger, J. (2013). Vaccine hesitancy: An overview. Human Vaccines & Immunotherapeutics, 9(8), 1763-1773. https://doi.org/10.4161/hv.24657 Duckett, L. J. (2021). Quantitative research excellence: Study design and reliable and valid measurement of variables. Journal of Human Lactation, 37(3), 456-463. https://doi.org/10.1177/08903344211019285 Durkin, L. F., Flynn, E. M., Johnson, M. L., Davies, W. H., & Greenly, R. N. (2023). Vaccine attitudes mediate relationships between caregiver political ideology and likelihood of child vaccination for COVID-19. Maternal and Child Health, 27(6), 984-990. https://doi.org/10.1007/s10995-023-03642-3 Gilkey, M. B., Magnus, B. E., Reiter, P. L., McRee, A.-L., Dempsey, A. F., & Brewer, N. T. (2014). The vaccination confidence scale: A brief measure of parents’ vaccination beliefs. Vaccine, 32(47), 6259-6265. https://doi.org/10.1016/j.vaccine.2014.09.007 Goldman, R. D., Yan, T. D., Seiler, M., Cotanda, C. P., Brown, J. C., Klein, E. J., Hoeffe, J., Gelernter, R., Hall, J. E., Davis, A. L., Griffiths, M. A., Mater, A., Manzano, S., Gualco, G., Shimizu, N., Hurt, T. L., Ahmed, S., Hansen, M., Sheridan, D., . . . For the International COVID-19 Parental Attitude Study (COVIPAS) Group. (2020). Caregiver 91 willingness to vaccinate their children against COVID-19: Cross sectional survey. Vaccine, 38(48), 7668-7673. https://doi.org/10.1016/j.vaccine.2020.09.084 Government of British Columbia. (2021). Safe, effective COVID-19 pediatric vaccine available for children aged 5-11. https://news.gov.bc.ca/25807 Government of British Columbia. (2022). B.C. encourages parents to register children under 5 for COVID-19 vaccine. https://news.gov.bc.ca/27179 Government of British Columbia. (n.d.). About B.C.’s health care system. Retrieved June 14, 2023 from https://www2.gov.bc.ca/gov/content/health/about-bc-s-health-care-system Government of Canada. (2022, June 1). COVID-19 signs, symptoms and severity of disease: A clinician guide. Retrieved Feb 14, 2024 from https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection/guidance-documents/signs-symptoms-severity.html Government of Canada. (2023a, April 14). COVID-19: Prevention and risks. Retrieved June 1, 2023 from https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection/prevention-risks.html Government of Canada. (2023b, March 27). Individual tax statistics by tax bracket 2022 edition (2020 tax year). Retrieved Jan 14, 2024 from https://www.canada.ca/en/revenue-agency/programs/about-canada-revenue-agency-cra/income-statistics-gst-hst-statistics/individual-tax-statistics-tax-bracket/individual-tax-statistics-tax-bracket-2022-edition-2020-tax-year.html Government of Canada. (2023c, January 20). Vaccines for children: COVID-19. Retrieved Feb 2, 2023 from https://www.canada.ca/en/public-health/services/vaccination-children/covid-19.html#anchor-2 92 Government of Canada. (2024, March 8). COVID-19 vaccination: Vaccination coverage. Retrieved March 19, 2024 from https://health-infobase.canada.ca/covid-19/vaccination-coverage/ Habersaat, K., MacDonald, N. E., & Dubé, È. (2021). Designing tailored interventions to address barriers to vaccination. Canada Communicable Disease Report, 47(3), 165-168. https://doi.org/10.14745/ccdr.v47i03a07 Hammershaimb, E. A., Cole, L. D., Liang, Y., Hendrich, M. A., Das, D., Petrin, R., Cataldi, J. R., O'Leary, S. T., & Campbell, J. D. (2022). COVID-19 vaccine acceptance among US parents: A nationally representative survey. Journal of the Pediatric Infectious Diseases Society, 11(8), 361-370. https://doi.org/10.1093/jpids/piac049 Hawthorne, G., & Elliot, P. (2005). Imputing cross-sectional missing data: Comparison of common techniques. Australian and New Zealand Journal of Psychiatry 39(7), 583-590. https://doi.org/10.1111/j.1440-1614.2005.01630.x He, K., Nack, W. K., Neely, M., Lewis, L., & Anand, V. (2022). Parental perspectives on immunizations: Impact of the COVID-19 pandemic on childhood vaccine hesitancy. Journal of Community Health, 47(1), 39-52. https://doi.org/10.1007/s10900-021-01017-9 Health Canada. (2021). Health Canada authorizes use of Comirnaty (the Pfizer-BioNTech COVID-19 vaccine) in children 5 to 11 years of age. https://www.canada.ca/en/health-canada/news/2021/11/health-canada-authorizes-use-of-comirnaty-the-pfizer-biontech-covid-19-vaccine-in-children-5-to-11-years-of-age.html Health Canada. (2022). Health Canada authorizes use of Moderna COVID-19 vaccine in children 6 months to 5 years of age. https://www.canada.ca/en/health-93 canada/news/2022/07/health-canada-authorizes-use-of-moderna-covid-19-vaccine-in-children-6-months-to-5-years-of-age.html Helmkamp, L. J., Szilagyi, P. G., Zimet, G., Saville, A. W., Gurfinkel, D., Albertin, C., Breck, A., Vangala, S., & Kempe, A. (2021). A validated modification of the vaccine hesitancy scale for childhood, influenza and HPV vaccines. Vaccine, 39(13), 1831-1839. https://doi.org/10.1016/j.vaccine.2021.02.039 Henrich, N., & Holmes, B. (2009). The public’s acceptance of novel vaccines during a pandemic: A focus group study and its application to influenza H1N1. Emerging Health Threats Journal, 2(1), 7088. https://doi.org/10.3402/ehtj.v2i0.7088 Hosmer, D. W., Hosmer, T., Le Cessie, S., & Lemeshow, S. (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, 16(9), 965-980. https://doi.org/10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509%3E3.0.CO;2-O Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). John Wiley and Sons. https://doi.org/10.1002/9781118548387 Humble, R. M., Sell, H., Dube, E., MacDonald, N. E., Robinson, J., Driedger, S. M., Sadarangani, M., Meyer, S. B., Wilson, S., Benzies, K. M., Samuel, L.-P., & MacDonald, S. E. (2021). Canadian parents’ perceptions of COVID-19 vaccination and intention to vaccinate their children: Results from a cross-sectional national survey. Vaccine, 39(52), 7669-7676. https://doi.org/10.1016/j.vaccine.2021.10.002 Humble, R. M., Sell, H., Wilson, S., Sadarangani, M., Bettinger, J. A., Meyer, S. B., Dube, E., Lemaire-Paquette, S., Gagneur, A., & MacDonald, S. E. (2022). Parents' perceptions on 94 COVID-19 vaccination as the new routine for their children ≤ 11 years old. Preventive Medicine, 161, 107125. https://doi.org/10.1016/j.ypmed.2022.107125 Interior Health. (n.d.). Health Authority Profile - 2021. Retrieved March 20, 2024 from https://www.interiorhealth.ca/sites/default/files/PDFS/interior-health-authority-profile.pdf Israel, G. D. (1992). Determining sample size. University of Florida Cooperative Extension Service Institue of Food and Agricultural Sciences. https://www.psycholosphere.com/Determining%20sample%20size%20by%20Glen%20Israel.pdf James, G., Witten, D., Hastie, T., & Robert, T. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer. https://doi.org/10.1007/978-1-0716-1418-1 Joanna Briggs Institute. (2017). Checklist for analytical cross-sectional studies. https://jbi.global/sites/default/files/2019-05/JBI_Critical_Appraisal-Checklist_for_Analytical_Cross_Sectional_Studies2017_0.pdf Kellar, S. P., & Kelvin, E. A. (2013). Munro’s statistical methods for health care research (6th ed.). Lippincott William & Wilkins. Kempe, A., Saville, A. W., Albertin, C., Zimet, G., Breck, A., Helmkamp, L., Vangala, S., Dickinson, L. M., Rand, C., Humiston, S., & Szilagyi, P. G. (2020). Parental hesitancy about routine childhood and influenza vaccinations: A national survey. Pediatrics, 146(1), 1-12. https://doi.org/10.1542/peds.2019-3852 Lachance-Grzela, M., Charbonneau, A., Dubé, A., Jbilou, J., & Richard, J. (2022). Parents and caregivers’ willingness to vaccinate their children against COVID-19. Canadian Journal of Behavioural Science, 55(4), 335-340. https://doi.org/10.1037/cbs0000333 95 Larson, H. J., Jarrett, C., Eckersbegerer, E., Smith, D. M. D., & Paterson, P. (2014). Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: A systematic review of published literature, 2007-2012. Vaccine, 32(19), 2150-2159. https://doi.org/10.1016/j.vaccine.2014.01.081 Larson, H. J., Jarrett, C., Schulz, W. S., Chaudhuri, M., Zhou, Y., Dube, E., Schuster, M., MacDonald, N. E., Wilson, R., & The SAGE Working Group on Vaccine Hesitancy. (2015). Measuring vaccine hesitancy: The development of a survey tool. Vaccine, 33(34), 4165-4175. https://doi.org/10.1016/j.vaccine.2015.04.037 Lawlor, J., Thomas, C., Guhin, A. T., Kenyon, K., Lerner, M. D., UCAS Consortium, & Drahota, A. (2021). Suspicious and fradulent online survey participation: Introducing the REAL framework. Methodological Innovations, 14(3), 205979912110504. https://doi.org/10.1177/20597991211050467 Lewis-Beck, C., & Lewis-Beck, M. (2016). Applied regression: An introduction (2nd ed.). SAGE Publications. https://doi.org/10.4135/9781483396774 Li, T., Qiu, X., Gong, X., Zhan, R., & Zheng, X. (2022). The cross-sectional survey on COVID-19 vaccine hesitancy and it predictors among Chinese parents of 3-17 years aged children in Shenzhen City. Annals of Agricultural and Environmental Medicine, 29(1), 120-125. https://doi.org/10.26444/aaem/146263 Limbers, C. A., & Thompson, R. (2022). Maternal attitudes and intentions about the COVID-19 vaccine for children aged 5-11 years. Journal of Pediatric Health Care, 36(5), 416-429. https://doi.org/10.1016/j.pedhc.2022.05.015 Ma, L.-L., Wang, Y.-Y., Yang, Z.-H., Huang, D., Weng, H., & Zeng, X.-T. (2020). Methodological quality (risk of bias) assessment tools for primary and secondary medical 96 studies: What are they and which is better? Military Medical Research, 7(7), 1-11. https://doi.org/10.1186/s40779-020-00238-8 MacDonald, N. E., & The SAGE Working Group on Vaccine Hesitancy. (2015). Vaccine hesitancy: Definition, scope and determinants. Vaccine, 33(34), 4161-4164. https://doi.org/10.1016/j.vaccine.2015.04.036 Martinez, E. Z., Zucoloto, M. L., Ramos, V. P., Dutra, C. D. C., de Jesus, G. J., Esteves, A. V. F., Abreu, I. S., Mombelli, M. A., Reis, R. A., Campoamor, M. M., da Silva, W. R., & Dos Santos, C. B. (2022). Brazilian adults’ attitudes and practices regarding the mandatory COVID-19 vaccination and their hesitancy towards childhood vaccination. Vaccines, 10(11), 1853. https://doi.org/10.3390/vaccines10111853 Nguyen, K. H., Yankey, D., Lu, P.-j., Kriss, J. L., Brewer, N. T., Razzaghi, H., Meghani, M., Manns, B. J., Lee, J. T., & Singleton, J. A. (2021). Report of health care provider recommendation for COVID-19 vaccination among adults, by recipient COVID-19 vaccination status and attitudes — United States, April–September 2021. Morbidty and Mortality Weekly Report, 70(50), 1723-1730. https://www.cdc.gov/mmwr/volumes/70/wr/mm7050a1.htm?s_cid=mm7050a1_w#suggestedcitation Olson, O., Berry, C., & Kumar, N. (2020). Addressing parental vaccine hesitancy towards childhood vaccines in the United States: A systematic literature review of communication interventions and strategies. Vaccines, 8(4), 590. https://doi.org/10.3390/vaccines8040590 Omair, A. (2015). Selecting the appropriate study design for your research: Descriptive study designs. Journal of Health Specialties, 3(3). https://doi.org/10.4103/1658-600X.159892 97 Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffman, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hrobartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., . . . Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(71), 178-189. https://doi.org/10.1186/s13643-021-01626-4 Paul, L. A., Daneman, N., Schwartz, K. L., Science, M., Brown, K. A., Whelan, M., Chan, E., & Buchan, S. A. (2021). Association of age and pediatric household transmission of SARS-Co-V-2 infection. JAMA Pediatrics, 175(11), 1151-1158. https://doi.org/10.1001/jamapediatrics.2021.2770 Peretti-Watel, P., Larson, H. J., Ward, J. K., Schulz, W. S., & Verger, P. (2015). Vaccine hesitancy: Clarifying a theoretical framework for an ambiguous notion. PLoS Currents, 7(7). https://doi.org/10.1371/currents.outbreaks.6844c80ff9f5b273f34c91f71b7fc289 Pourhoseingholi, M. A., Vahedi, M., & Rahimzadeh, M. (2013). Sample size calculation in medical studies. Gastroenterology and Hepatology From Bed to Bench, 6(1), 14-17. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017493/ Salmon, D. A., Dudley, M. Z., Glanz, J. M., & Omer, S. B. (2015). Vaccine hesitancy causes, consequences, and a call to action. Vaccine, 22(6), 391-398. https://doi.org/10.1016/j.vaccine.2015.09.035 Sarkar, P., Chandrasekaran, V., Gunasekaran, D., & Chinnakali, P. (2022). COVID-19 vaccine hesitancy among health care worker-parents (HCWP) in Puducherry, India and its implications on their children: A cross sectional descriptive study. Vaccine, 40(40), 5821-5827. https://doi.org/10.1016/j.vaccine.2022.08.051 98 Shapiro, G. K., Tarta, O., Dube, E., Amsel, R., Knauper, B., Naz, A., Perez, S., & Rosberger, Z. (2018). The vaccine hesitancy scale: Psychometric properties and validation. Vaccine, 36(5), 660-667. https://doi.org/10.1016/j.vaccine.2017.12.043 Shekerdemian, L. S., Mahmood, N. R., Wolfe, K. K., Riggs, B. J., Ross, C. E., McKiernan, C. A., Heiemann, S. M., Kleinman, L. C., Sen, A. I., Hall, M. W., Priestley, M. A., McGuire, J. K., Boukas, K., Sharron, M. P., & Burns, J. P. (2020). Characteristics and outcomes of children with coronavirus disease 2019 (COVID-19) infection admitted to US and Canadian pediatric intensive care units. JAMA Pediatrics, 174(9), 869-873. https://doi.org/10.1001/jamapediatrics.2020.1948 Skeens, M., Guttoo, P., Stanek, J. R., Taylor, K., Stratz, E., Ardura, M. I., & Rangarajan, H. G. (2022a). An exploration of COVID-19 impact and vaccine hesitancy in parents of pediatric hematopoietic stem cell transplant (HCT) recipients. Bone Marrow Transplant, 57(4), 547-553. https://doi.org/10.1038/s41409-022-01587-9 Skeens, M., Hill, K., Olsavsky, A., Buff, K., Stevens, J., Akard, T. F., Shah, N., & Ferhardt, C. A. (2022b). Factors affecting COVID-19 vaccine hesitancy in parents of children with cancer. Pediatric Blood & Cancer, 69(6), e29707. https://doi.org/10.1002/pbc.29707 Srivastava, T., Shen, A. K., Browne, S., Michel, J. J., Tan, A. S. L., & Kornides, M. L. (2022). Comparing COVID-19 vaccination outcomes with parental values, beliefs, attitudes, and hesitancy status, 2021-2022. Vaccines, 10(10), 1632. https://doi.org/10.3390/vaccines10101632 StataCorp. (2023). Stata Statistical Software: Release 18. In College Station, Tx: StatCorp LLC. 99 Statistics Canada. (2022a). COVID-19 in Canada: A two-year update on social and economic impacts. Retrieved from https://www150.statcan.gc.ca/n1/en/pub/11-631-x/11-631-x2022001-eng.pdf?st=UqCEiFBG Statistics Canada. (2022b, February 9). Population and dwelling counts: Canada, provinces and territories. Retrieved January 14, 2024 from https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=9810000101 Statistics Canada. (2023, November 15). Census profile: Census of population. Retrieved January 31, 2024 from https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/details/page.cfm?Lang=E&SearchText=British%20Columbia&DGUIDlist=2021A000259&GENDERlist=1,2,3&STATISTIClist=1,4&HEADERlist=0 Szilagyi, P. G., Shah, M. D., Delgado, J. R., Thomas, K., Vizueta, N., Cui, Y., Vangala, S., Shetgiri, R., & Kapteyn, A. (2021). Parents' intentions and perceptions about COVID-19 vaccination for their children: Results from a national survey. Pediatrics, 148(4). https://doi.org/10.1542/peds.2021-052335 Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2, 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd Temsah, M.-H., Alhuzaimi, A. N., Aljamaan, F., Bahkali, F., Al-Eyadhy, A., Alrabiaah, A., Alhaboob, A., Bashiri, F. A., Alshaer, A., Temsah, O., Bassrawi, R., Alshahrani, F., Chaiah, Y., Alaraj, A., Assiri, R. A., Jamal, A., Batai, M. A., Saddik, B., Halwani, R., . . . Alhasan, K. (2021). Parental attitudes and hesitancy about COVID-19 vs. routine childhood vaccinations: A national survey. Frontiers in Public Health, 9, 752323. https://doi.org/10.3389/fpubh.2021.752323 100 Thiara, S., Henry, B., Patrick, D., & Kanji, H. (2020). British Columbia’s COVID-19 experience. BC Medical Journal, 62(8), 277-279. https://bcmj.org/sites/default/files/BCMJ_Vol62_No8_covid-19.pdf Villarosa, A. R., Maneze, D., Ramjan, L. M., Srinivas, R., Camilleri, M., & George, A. (2019). The effectiveness of guideline implementation strategies in the dental setting: A systematic review. Implementation Science, 14(1), 106. https://doi.org/10.1186/s13012-019-0954-7 Waddell, C., Schwartz, C., Barican, J., Yung, D., & Gray-Grant, D. (2020). COVID-19 and the impact on children’s mental health: A research report. Vancouver, BC: Children’s Health Policy Centre, Simon Fraser University. https://summit.sfu.ca/item/31711 Wang, C. S., Doma, R., Westbrook, A. L., Johnson, J., Anderson, E. J., Greenbaum, L. A., Rana, S. A., George, R. P., Garro, R., Khanna-Farber, A., Escoffery, C., & Bednarczyk, R. A. (2023). Vaccine attitudes and COVID-19 vaccine intention among parents of children with kidney disease or primary hypertension. American Journal of Kidney Diseases, 81(1), 25-35. https://doi.org/10.1053/j.ajkd.2022.04.011 Wang, Q., Xiu, S., Yang, L., Han, Y., Cui, T., Shi, N., Liu, M., Yi, Y., Liu, C., Wang, X., Yang, G., Ji, L., Zhou, W., Jin, H., Zhen, S., & Lin, L. (2022). Changes in parental attitudes toward COVID-19 vaccination and routine childhood vaccination during the COVID-19 pandemic: Repeated cross-sectional survey study. JMIR Public Health and Surveillance, 8(5), e33235. https://doi.org/10.2196/33235 Wang, Q., Xiu, S., Zhao, S., Wang, J., Han, Y., Dong, S., Huang, J., Cui, T., Yang, L., Shi, N., Liu, M., Han, Y., Wang, X., Shen, Y., Chen, E., Lu, B., Jin, H., & Lin, L. (2021). Vaccine hesitancy: COVID-19 and influenza vaccine willingness among parents in Wuxi, 101 China — A cross-sectional study. Vaccines, 9(4), 342. https://doi.org/10.3390/vaccines9040342 Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations. Chest, 158(1), 65-71. https://doi.org/10.1016/j.chest.2020.03.012 World Health Organization. (2014). Report of the SAGE Working Group on Vaccine Hesitancy. https://www.asset-scienceinsociety.eu/sites/default/files/sage_working_group_revised_report_vaccine_hesitancy.pdf World Health Organization. (2015). Summary WHO SAGE conclusion and recommendations on vaccine hesitancy. https://cdn.who.int/media/docs/default-source/immunization/demand/summary-of-sage-vaccinehesitancy-en.pdf?sfvrsn=abbfd5c8_2 World Health Organization. (2019a). Tailoring immunization programmes. https://iris.who.int/bitstream/handle/10665/329448/9789289054492-eng.pdf?sequence=1 World Health Organization. (2019b, January 10). Ten threats to global health in 2019. Retrieved June 13, 2023 from https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019 World Health Organization. (2020). WHO director-general’s opening remarks at the media briefing on COVID-19. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 World Health Organization. (2021). Global COVID-19 Vaccination - Strategic Vision for 2022 [Technical Document]. https://cdn.who.int/media/docs/default-102 source/immunization/sage/covid/global-covid-19-vaccination-strategic-vision-for-2022_sage-yellow-book.pdf World Health Organization. (2024, March 3). WHO Coronavirus (COVID-19) Dashboard. Retrieved March 19, 2024 from https://covid19.who.int/ Yeo, J., Gudmundsen, C. F., Fazel, S., Corrigan, A., Fuller, M. M., Hu, J., Jadavji, T., Kuhn, S., Kassam, A., & Constantinescu, C. (2022). A behavior change model to address caregiver hesitancy around COVID-19 vaccination in pediatrics. Vaccine, 40(39), 5664-5669. https://doi.org/10.1016/j.vaccine.2022.08.002
Thesis/Dissertation
eng
Nursing
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Tarrant, Marie
Understanding COVID-19 vaccine perceptions during parenthood in British Columbia
Text