COVID-19 vaccination decisions and the impact of vaccination mandates: An exploratory cross-sectional survey of healthcare workers in British Columbia, Canada
Bibliographic record
Abstract
British Columbia implemented some of Canada’s strictest COVID-19 workplace vaccination mandates in the healthcare sector. Despite opposition from some healthcare worker (HCW) unions, most health establishments, policymakers, and academic researchers supported these mandates, which remained in effect until July 2024. While the perceived problem of vaccine hesitancy among HCWs has generated much research, HCWs’ experiences and views on vaccination mandates have been relatively neglected. Our study, conducted between June and July 2024, explored these experiences and views. We surveyed 166 HCWs recruited through social media and snowball sampling, regardless of vaccination status, age, gender, ethnicity, socioeconomic background, or health profession. Nearly half of the respondents had over 16 years of work experience, most were unvaccinated, and most had been terminated for non-compliance. Both unvaccinated and vaccinated respondents expressed concerns about vaccine safety, coercion, and mental health impacts, including suicidal thoughts. Most unvaccinated respondents were satisfied with their vaccination decision, but they reported financial losses, mental health struggles, and conflicts with colleagues and loved ones. The vaccinated respondents – a minority in our sample - were largely unsatisfied, with most experiencing post-vaccination adverse events, and over half feeling pressured by their employers to accept further doses despite these effects. Regardless of vaccination status, HCWs observed concerning changes in practice protocols and discrimination against unvaccinated patients. We argue that even if neglected by the literature, the multiple negative impacts of vaccination mandates identified in our study – on HCWs’ well-being, patient care, and ethical healthcare practices – are extremely worthy of consideration. They indicate the importance of prioritizing informed consent, engaging competing scientific evidence, and ensuring healthcare sustainability, particularly during emergencies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".