Evaluation of a comprehensive health check offered to frontline health workers in Zimbabwe
Bibliographic record
Abstract
Health workers are essential for a functioning healthcare system, and their own health is often not addressed. During the COVID-19 pandemic health workers were at high risk of SARS-CoV-2 infection whilst coping with increased healthcare demand. Here we report the development, implementation, and uptake of an integrated health check combining SARS-CoV-2 testing with screening for other communicable and non-communicable diseases for health workers in Zimbabwe during the COVID-19 pandemic. Health checks were offered to health workers in public and private health facilities from July 2020 to June 2022. Data on the number of health workers accessing the service and yield of screening was collected. Workshops and in-depth interviews were conducted to explore the perceptions and experiences of clients and service providers. 6598 health workers across 48 health facilities accessed the service. Among those reached, 5215 (79%) were women, the median age was 37 (IQR: 29-44) years and the largest proportion were nurses (n = 2092, 32%). 149 (2.3%) healthcare workers tested positive for SARS-CoV-2. Uptake of screening services was almost 100% for all screened conditions except HIV. The most common conditions detected through screening were elevated blood pressure (n = 1249; 19%), elevated HbA1c (n = 428; 7.7%) and common mental disorder (n = 645; 9.8%). Process evaluation showed high acceptability of the service. Key enablers for health workers accessing the service included free and comprehensive service provision, and availability of reliable point-of-care screening methods. Implementation of a comprehensive health check for health workers was feasible, acceptable, and effective, even during a pandemic. Conventional occupational health programmes focus on infectious diseases. In a society where even health workers cannot afford health care, free comprehensive occupational health services may address unmet needs in prevention, diagnosis, and treatment for chronic non-communicable conditions.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".