Is resisting Covid-19 vaccination a “problem”? A critical policy inquiry of vaccine mandates for healthcare workers
Bibliographic record
Abstract
As the COVID-19 global vaccination campaign was launched in December of 2020, vaccination became mandatory for many healthcare workers (HCWs) worldwide. Large minorities resisted the policy, and the responses of authorities to this resistance led to damaged professional reputations, job losses, and suspension or termination of practice licenses. The joint effect of dismissals, early retirements, career changes, and vaccine injuries disabling some compliant HCWs from adequate performance, has exacerbated existing crises within health systems. Nevertheless, the position of leading health authorities has been that the benefits of a fully vaccinated healthcare labour force - protecting health systems, vulnerable patient populations, and even HCWs themselves – achieved through mandates, if necessary, outweigh its potential harms. Informed by critical policy and discourse traditions, we examine the expert literature on vaccine mandates for HCWs. We find that this literature neglects evidence countering claims about the safety and effectiveness of COVID-19 vaccines, dismisses the science supporting the contextual nature of microbial virulence, miscalculates patient and system-level harms of vaccination policies, and ignores or legitimizes the coercive elements built into their design. We discuss the implications of our findings for the sustainability of health systems, for patient care, and for the well-being of HCWs, and suggest directions for ethical clinical and policy practice. (Update: Published in AIMS Public Health https://www.aimspress.com/article/doi/10.3934/publichealth.2024035)
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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 | 0.127 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.069 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".