Covid-19 Vaccination Decisions and Impacts of Vaccine Mandates: A Cross-Sectional Survey of Healthcare Workers in Alberta, Canada
Bibliographic record
Abstract
This paper presents findings from a cross-sectional survey of 189 healthcare workers (HCWs) in Alberta, Canada, regarding their experiences with Covid-19 vaccination and workplace mandates. Unlike most research, which continues to focus on vaccine uptake and hesitancy, this study examines the impact of mandate enforcement on healthcare professionals’ ethical judgment, workplace dynamics, and clinical autonomy. Alberta’s polarized political context, combined with rapidly changing public health policies, provides a unique lens into broader tensions around pandemic governance. While most respondents had been vaccinated, nearly one-third identified workplace mandates—not medical judgment—as the primary reason for doing so. Many reported feeling coerced, lacking meaningful informed consent, and experiencing adverse events that were inadequately documented or dismissed by their institutions. Respondents, regardless of vaccination status, described long-lasting professional, economic, and psychological harms, including deteriorating morale, disrupted teamwork, and increased mistrust—impacts that extended beyond those who were terminated. Respondents also reported observing patient harms associated with the vaccines, especially among low-risk populations where the balance of risks and benefits remains dubious. The survey complements previous work in Ontario and British Columbia, confirming that vaccine mandates contributed to workforce destabilization and ethical injury. Our findings suggest that coercive health policies—justified by public safety but unsupported by transparent evidence—undermine trust, autonomy, and system resilience. They call for a re-evaluation of public health practices to ensure alignment with scientific integrity and core healthcare ethics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".