Did the health care vaccine mandate work? An evaluation of the impact of the COVID-19 vaccine mandate on vaccine uptake and infection risk in a large cohort of Canadian health care workers
Bibliographic record
Abstract
BACKGROUND: We aimed to evaluate the impact of health care vaccine mandates on vaccine uptake and infection risk in a cohort of Canadian health care workers (HCWs). METHODS: We conduct interrupted time series analysis through a regression discontinuity in time approach to estimate the immediate and delayed impact of the mandate. Multilevel mixed effect modeling fitted with restricted maximum likelihood was used to estimate impact on infection risk. RESULTS: The immediate and sustained effects of the mandate was a 0.19% (P < .05) and a 0.012% (P < .05) increase in the daily proportion of unvaccinated HCWs getting their first dose, respectively. An additional 623 (95% confidence interval: 613-667) HCWs received first doses compared to the predicted uptake absent the mandate. Adjusted test positivity declined by 0.053% (95% confidence interval: 0.035%, 0.069) for every additional day the mandate was in effect. DISCUSSION: Our results indicate that the mandate was associated with significant increases in vaccine uptake and infection risk reduction in the cohort. CONCLUSIONS: Given the benefit that vaccination could bring to HCWs, understanding strategies to enhance uptake is crucial for bolstering health system resilience, but steps must be taken to avert approaches that sacrifice trust, foster animosity, or exacerbate staffing constraints for short-term results.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".