“If all of this was about HEALTH, I’d still be working”: Lived Experiences of Covid-19 Vaccine Mandates of Healthcare Workers in British Columbia, Canada
Bibliographic record
Abstract
Abstract Background COVID-19 vaccine mandates for healthcare workers (HCWs) in the Canadian province of British Columbia (BC) were implemented in October 2021. Despite opposition from some HCW unions and protests across the province, mandates remained effective until July 2024. This study examined the lived experience of HCWs in BC of COVID vaccine mandates, focusing on HCWs’ decision-making process, the mandates’ impact on their lives and livelihoods, and their views on the effects of mandates on patient care. Methods We performed a reflexive thematic analysis of responses to one open ended question and open-ended entries to closed questions from within a published survey of a convenience sample of HCWs in BC of diverse vaccination status, professions, ages, socioeconomic status, races/ethnicities, and genders. Respondents were recruited through snowball sampling via social media and professional networks of the research team. The study was approved by the York University Office of Research Ethics (No. 2023-389). The article is presented in accordance with the COREQ reporting checklist. Results Textual data from 90 HCWs were collected from within the initial 166 respondents to the survey. Most respondents were unvaccinated, had been terminated for non-compliance with vaccination mandates, experienced personal losses, and reported negative views on mandates and their impacts on patient care. We identified six themes: 1) policies conflicting with scientific evidence and professional practice; 2) conflicts with medical ethics; 3) unacknowledged or dismissed personal hardships; 4) unacknowledged or dismissed physical harms; 5) discrimination against unvaccinated HCWs and patients; and 6) negative impacts on patient care. Conclusions This study uncovered a concerning pattern within British Columbia’s healthcare system, with mandatory vaccination leading to substantial social and economic harms for HCWs of all vaccination statuses as well as unvaccinated patients. Mandates appear to have exacerbated labour shortages in the healthcare sector, negatively impacted workplace morale and the quality of patient care, and eroded informed consent. In light of these findings and of available scientific evidence then and now, we conclude that the policy of mandatory vaccination for HCWs has no scientific basis and violates fundamental ethical principles in healthcare practice and policy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".