It isn't about health, and it sure doesn't care: A qualitative exploration of healthcare workers’ experience of the policy of vaccination mandates in Ontario, Canada
Bibliographic record
Abstract
Background: When COVID-19 vaccines became available, healthcare workers (HCWs) were prioritized for vaccination. Despite opposition from sectors of the population and of the healthcare labour force itself, vaccine mandates were implemented in most healthcare settings across Canada, including Ontario, with many still in effect. Goal: This study examined Ontario HCWs' lived experience of vaccine mandates, focusing on their decision-making processes, the mandates' impact on their lives and livelihoods, and their views on the effects of mandates on patient care.Methods: We conducted an online cross-sectional survey among Ontario HCWs of diverse vaccination status, professions, ages, socioeconomic status, races/ethnicities, and genders, recruiting through professional networks and snowball sampling. For this article, we performed a thematic analysis of responses to one open-ended question and to open-ended options in closed questions. We reported quantitative results separately.Results: Out of 468 respondents, 225 filled out the open-ended question. We further collected 110 qualitative entries from the open-ended options to closed questions, 20 from respondents who had not filled out the open-ended question. The final sample, that included both sources of data, represented a total of 245 respondents. 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: Our study revealed a system in Ontario healthcare settings that inflicts significant harm on non-compliant HCWs and patients, discriminates against these workers’ right to work, and violates the bioethical principle of informed consent of both HCWs and patients. We conclude that mandated vaccination must be ended and replaced with evidence- and ethics-informed healthcare workplace policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".