Are COVID-19 vaccination mandates for healthcare workers effective? A systematic review of the impact of mandates on increasing vaccination, alleviating staff shortages and decreasing staff illness
Bibliographic record
Abstract
INTRODUCTION: The rapid development of COVID-19 vaccines is a cornerstone in the global effort to combat the pandemic. Healthcare workers (HCWs), being at the forefront of the pandemic response, have been the focus of vaccine mandate policies. This review aims to evaluate the impacts of COVID-19 vaccine mandates among HCWs, a critical step in understanding the broader implications of such policies in healthcare settings. OBJECTIVE: The review seeks to synthesize available literature to contribute to greater understanding of the outcomes associated with COVID-19 vaccine mandates for HCWs including vaccine uptake, infection rates, and staffing. METHODS: A systematic search of relevant literature published from March 2020 to September 2023 was conducted. The Newcastle-Ottawa scale was employed for quality assessment of the included articles. A total of 4,779 publications were identified, with 15 studies meeting the inclusion criteria for the review. A narrative synthesis approach was used to analyze these studies. RESULTS: COVID-19 vaccine mandates for HCWs were broadly successful in increasing vaccine uptake in most settings. Although the penalties imposed on unvaccinated HCWs did not lead to major disruption of health services, less well-resourced areas may have been more impacted. Furthermore, there is insufficient literature on the impact of the vaccine mandate on reducing SARS-CoV-2 infection among HCWs. CONCLUSION: COVID-19 vaccine mandates for HCWs have significant implications for public health policy and healthcare management. The findings underscore the need for tailored approaches in mandate policies, considering the specific contexts of healthcare settings and the diverse populations of HCWs. While mandates have shown potential in increasing vaccine uptake with minimal impacts to staffing, more work is needed to investigate the impacts of mandates across various contexts. In addition to these impacts, future research should focus on long-term effects and implications on broader public health strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".