How did Ontario healthcare institutions implement and legitimize Covid-19 vaccine mandates? A mixed-methods study protocol
Bibliographic record
Abstract
Abstract Background Upon the WHO declaration of COVID-19 as a pandemic, healthcare workers (HCWs) - unvaccinated by necessity - were celebrated as “heroes” for their service under difficult conditions. Later, as some of them resisted vaccine mandates, they were reframed as “threats”, regardless of personal behaviour, workplace setting, or evidence of their harmfulness. This discursive shift and the institutional mechanisms supporting it remain underexamined. Goal This protocol outlines a mixed-method study investigating the implementation of COVID-19 vaccine mandates in medical establishments across Ontario, Canada. Methods This is a two-phase mixed methods planned study. Phase 1 is an environmental scan of institutional vaccine mandate policies across a purposive sample of diverse Ontario medical establishments. It will track policy implementation timelines, mandates scope, exemptions eligibility criteria, and supporting scientific evidence presented. Phase 2 is a critical interpretive analysis of documents collected in Phase 1 that draws on Max Weber’s theory of bureaucracy and legitimacy, Carol Bacchi’s “What Is the Problem Represented to Be” (WPR) approach, and Brian Martin’s framework on suppression of dissent to identify patters in the institutional framing and treatment of challenges to mandated vaccination. Expected Outcomes This study is expected to yield descriptive and interpretive insights into how bureaucratic structures shaped mandate enforcement and dissent suppression. Results are expected to inform academic debates on institutional legitimacy, governance, and public health 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 | 0.089 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".