Vaccine hesitancy among older adults, urban indigenous and newcomers in Canada: A qualitative comparative study
Bibliographic record
Abstract
BACKGROUND: As seen during the COVID-19 pandemic, infectious diseases disproportionally affect some subgroups that are often prioritized by publicly funded vaccination programs. Vaccine hesitancy could jeopardize the success of these tailored vaccination efforts. The goal of this study was to explore and compare vaccine hesitancy against COVID-19 in the context of the pandemic in the province of Quebec among three distinct groups: older adults, Indigenous People in urban settings and newcomers. METHODS: This study is part of a larger qualitative project on information practices for which data had already been collected and transcribed. Participants were recruited through a recruitment database and various community organizations. Eighty-four individual interviews were conducted between summer 2021 and spring 2022. A thematic content analysis was conducted using NVivo Software. The coding was guided by the Vaccine Hesitancy Determinants Matrix created by the World Health Organization. RESULTS: Among the 79 interviews included in the analysis, the distribution was as follows: older adults (n = 36), Indigenous People in urban settings (n = 24) and newcomers (n = 19). The identified determinants of vaccine hesitancy included vaccine passport and other vaccines mandates, health checkpoints, historical influences, trust in the government and health authorities, perceived risk around COVID-19, peers' opinions and risks and benefits of the vaccines. The study highlighted vaccine hesitancy's complexity while demonstrating how underlying factors of hesitancy can vary across different groups. The study also depicted aspects of vaccine hesitancy rarely addressed in literature, such as roadblocks and checkpoints at the entrance of Indigenous communities. CONCLUSION: This qualitative study is one of the few studies exploring and comparing vaccine hesitancy against COVID-19 among older adults, Indigenous People in urban settings and newcomers. The results could contribute to the development of tailored and culturally safe vaccination programs, particularly during the introduction of a new vaccine or in the event of a future pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".