“When I had concerns about my own patients…I was told to keep quiet”: Moral Injury in the Era of Mandates Among Healthcare Workers in Alberta, Canada
Bibliographic record
Abstract
Abstract Between 2021 and early 2022, vaccine mandates in Alberta, Canada, became among the most stringent in the country. This qualitative study explores the lived experiences of healthcare workers (HCWs) following the implementation of Covid-19 vaccine mandates in Alberta’s health sector. It draws on 80 responses to a single open-ended question from a survey of 190 HCWs in the province across vaccination statuses. We performed a manual sentiment analysis, classifying entries as positive, neutral, or negative - depending on their normative orientation towards vaccination mandates - using Weberian ideal types as a conceptual framework. Most respondents (82.5%) expressed negative sentiments, with close to one fifth (17.5%) offering positive views; no entries were coded as neutral. Themes within negative responses included coercion, ethical conflict, professional exclusion, institutional betrayal, and suppression of dissent. Many vaccinated HCWs described complying under duress, challenging the assumption that uptake signals endorsement. The most salient theme was that of moral injury - defined as the distress caused by acting against one’s conscience, witnessing perceived harm, or remaining silent under institutional pressure. In contrast, positive responses emphasized professional duty and public safety, often rejecting the legitimacy of dissenting perspectives. Our findings underscore the deeply polarizing nature of vaccination mandates and complicate dominant narratives that equate compliance with consent. Further, in contrast to clinical conceptions rooted in combat or bedside trauma, our analysis situates moral injury in the structural conditions created by public health policies, offering a lens for assessing their implications for the wellbeing of HCWs, quality care, and ethical healthcare practice and policy. We conclude that future public health policies, especially those justified under claims of emergency, must include built-in safeguards for ethical integrity and democratic participation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".