Scope and Spatio-Temporal Patterns of Workplace Vaccination Mandates During the COVID-19 Pandemic
Bibliographic record
Abstract
The global response to the COVID-19 pandemic was characterized by a patchwork of government policies in countries around the world, many of which limited civil liberties in unprecedented ways. Here, our objective was to analyze the scope and spatio-temporal patterns of workplace vaccination mandates. Using daily policy data from the Oxford COVID-19 Government Response Tracker for 2021-2022, we developed a simple mandate intensity index representing the number of affected employment sectors and the duration of each mandate by country. These metrics suggest a largely inconsistent pandemic response. We found that less than one-third of the 185 countries included in the dataset implemented such "no jab, no job" policies. Even among groups of culturally and politically aligned countries, such as the core Anglosphere, policies varied greatly: between one (United Kingdom) and 10 (Australia) out of 12 employment sectors had vaccination mandates. The most frequently and longest mandated sectors included government officials and healthcare workers, two broad groups with different risk profiles. We discuss these discrepancies from a critical perspective, considering the limited evidence for the mandates' effectiveness along with their potential to cause harmful outcomes, and recommend careful cost-benefit analyses in the future.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".