Empowering Health Workers to Protect their Own Health: A Study of Enabling Factors and Barriers to Implementing HealthWISE in Mozambique, South Africa, and Zimbabwe
Bibliographic record
Abstract
Ways to address the increasing global health workforce shortage include improving the occupational health and safety of health workers, particularly those in high-risk, low-resource settings. The World Health Organization and International Labour Organization designed HealthWISE, a quality improvement tool to help health workers identify workplace hazards to find and apply low-cost solutions. However, its implementation had never been systematically evaluated. We, therefore, studied the implementation of HealthWISE in seven hospitals in three countries: Mozambique, South Africa, and Zimbabwe. Through a multiple-case study and thematic analysis of data collected primarily from focus group discussions and questionnaires, we examined the enabling factors and barriers to the implementation of HealthWISE by applying the integrated Promoting Action on Research Implementation in Health Services (i-PARiHS) framework. Enabling factors included the willingness of workers to engage in the implementation, diverse teams that championed the process, and supportive senior leadership. Barriers included lack of clarity about how to use HealthWISE, insufficient funds, stretched human resources, older buildings, and lack of incident reporting infrastructure. Overall, successful implementation of HealthWISE required dedicated local team members who helped facilitate the process by adapting HealthWISE to the workers' occupational health and safety (OHS) knowledge and skill levels and the cultures and needs of their hospitals, cutting across all constructs of the i-PARiHS framework.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".