Protecting healthcare workers during a pandemic: what can a WHO collaborating centre research partnership contribute?
Bibliographic record
Abstract
Objectives: To ascertain whether and how working as a partnership of two World Health Organization collaborating centres (WHOCCs), based respectively in the Global North and Global South, can add insights on "what works to protect healthcare workers (HCWs) during a pandemic, in what contexts, using what mechanism, to achieve what outcome". Methods: A realist synthesis of seven projects in this research program was carried out to characterize context (C) (including researcher positionality), mechanism (M) (including service relationships) and outcome (O) in each project. An assessment was then conducted of the role of the WHOCC partnership in each study and overall. Results: The research found that lower-resourced countries with higher economic disparity, including South Africa, incurred greater occupational health risk and had less acceptable measures to protect HCWs at the onset of the COVID-19 pandemic than higher-income more-equal counterpart countries. It showed that rigorously adopting occupational health measures can indeed protect the healthcare workforce; training and preventive initiatives can reduce workplace stress; information systems are valued; and HCWs most at-risk (including care aides in the Canadian setting) can be readily identified to trigger adoption of protective actions. The C-M-O analysis showed that various ways of working through a WHOCC partnership not only enabled knowledge sharing, but allowed for triangulating results and, ultimately, initiatives for worker protection. Conclusions: The value of an international partnership on a North-South axis especially lies in providing contextualized global evidence regarding protecting HCWs as a pandemic emerges, particularly with bi-directional cross-jurisdiction participation by researchers working with practitioners.
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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 | 0.283 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.036 | 0.033 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".