Perceptions of nurses on occupational health hazards and safety practices in Ditsobotla public hospitals in North West province
Bibliographic record
Abstract
BACKGROUND: Nurses are the backbone of the healthcare system. During the fulfilment of their duties and responsibilities, they experience various types of work-related risks, which harmfully affect their health and nursing quality. OBJECTIVES: This study aimed to explore and describe perceptions of nurses on occupational health hazards and safety practices in Ditsobotla public hospitals of North West province. METHOD: An exploratory, descriptive, qualitative research design was undertaken in this study. An explorative design allowed the researcher to identify key issues regarding nurses' perceptions on occupational health hazards and safety practices using Donabedian structure, process and outcome. A total of 15 nurses of different categories participated in the study that formed four focus group discussions. Semi-structured focus group discussions of three to four participants were conducted until data saturation is obtained and at the point where no new themes from participants' perceptions emerged. Creswell and Clark framework of data analysis was used to analyse data. RESULTS: Three major categories emerged during data analysis: nurse's perception on occupational health hazards in the health settings, Donabedian framework on assessing the quality of care in relation to occupational health hazards and occupational health and safety (OHS) practices. Eight themes were identified. CONCLUSION: Nurses' perceived different occupational hazards that affect their normal duties and responsibility in the workplace. Future interventions such as training and education on OHS policy should be adopted to promote health and well-being of the staff.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".