Health worker protests and the COVID-19 pandemic: an interrupted time-series analysis
Bibliographic record
Abstract
Objective: To assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on protests by health workers. Methods: We conducted an interrupted time series analysis of data from 159 countries for 2 years before and after the World Health Organization classified COVID-19 as a pandemic in March 2020, thus between 2018 and 2022. We produced models examining two main outcomes: (i) the total weekly number of health worker protests globally; and (ii) the number of countries with one or more health worker protests in a given week. Findings: In total, there were 18 322 health worker protests in 133 countries between 2018 and 2022. The number of weekly health worker protests globally increased by 47% (30.1/63.5), an increase of 30.1 protests per week (95% confidence interval, CI: 11.7-48.6) at the onset of the COVID-19 pandemic. Furthermore, the number of countries experiencing such protests in a given week increased by 24% (5.7/24.1) following the declaration of the pandemic (an increase of 5.7 countries; 95% CI: 3.5-7.8). Conclusion: The pandemic increased the overall level of health worker protests globally as well as the number of countries experiencing such protests. These protests highlight discontent in the health workforce. Given the ongoing global health workforce crisis, understanding and addressing the drivers of health worker discontent is important for global health policy and security.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".