Selected features of nurses' occupational health and safety practice in three Free State provincial public hospitals : original research
Bibliographic record
Abstract
Background: Despite growing research, knowledge about nurses' occupational health and safety status, and engagement with occupational health and safety (OHS), services remain sparse. Objectives: The aim of this research was to explore the OHS of nurses in three Free State hospitals, focusing on OHS-related training, vaccinations and work examinations, OHS-related practices, nurses' engagement with hospital-based management, OHS services, and health and safety representatives. Methods: A cross-sectional baseline survey was conducted among Free State public hospitals. Data were collected from a sample of 363 nurses, using self-administered questionnaires. Results: One-fifth (22.3%) of nurses had experienced sharps injuries; vaccination rates were low; and 69.0% had not been screened for tuberculosis at work. A large proportion (38.5%) of nurses reported always recapping needles (a hazardous process). One-quarter (24.9%) never wore N95 respirators when required. The majority (89.8%) were aware of the procedure for contacting the occupational health services but 27.5% did not know how to report an occupational injury or disease. Conclusion: The study highlights the precarious working contexts of nursing in Free State hospitals, adding to a growing body of knowledge on public hospital OHS in low-to-middle income countries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".