Knowledge and practices among registered nurses on occupational hazards in Onandjokwe Health District: Oshikoto region, Namibia
Bibliographic record
Abstract
Objective: This study sought to explore and describe the extent of the knowledge on occupational hazards amongst registered nurses in Onandjokwe Health District.Methods: This study used a quantitative research design utilizing a survey by means of a questionnaire. The population of the study consisted of randomly selected registered nurses who were in direct contact with patients. Data was gathered using questionnaires, utilized for descriptive statistics and evaluated with quantitative, computerised statistical techniques using. Statistical Package for the Social Scientists (SPSS).Results: The findings of this study revealed that the majority of registered nurses have knowledge on occupational hazards, yet a few number (24%) have insufficient knowledge. The findings also revealed that information and support is provided to some (22%) nurses.Recommendations: Recommendations made based on the findings of this study include regular training and educational meetings to enhance occupational safety, develop/introduce policies and guidelines or strategies on all aspects related to occupational hazards/safety.Conclusions: Most (76%) of the registered nurses have knowledge on the ways that they can be exposed to occupational hazards in the following areas: such as handling of sharp instruments, lifting of patients, exposure to psychological problems due to frustrations, and exposure airborne diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".