Factors Influencing the Practices of Health Care Workers on Prevention and Control of Infection at Keetmanshoop District Hospital, Namibia
Bibliographic record
Abstract
INTRODUCTION: According to World Health Organisation, prevention and control of infection is a strategy designed to protect both patients and health care workers from infections. Lack of such strategy among health care workers has negative impact such as long-term hospitalization, death, and morbidity. Therefore, the aim of this study was to determine the practices of health care workers on prevention and control of infection at Keetmanshoop district Hospital. METHOD: A descriptive correlational cross-sectional study design was employed. Structured questionnaire was used to collect data from participants. Data collected were analysed using SPSS version 27. Multiple regression analysis was used to determine, the factors influencing the practices of health care workers on prevention and control of infection. RESULTS: The findings shows that more than 50% of the health care workers in Keetmanshoop district hospital have poor adherence to IPC. However, factors such as demographic characteristics and resources availability do not have any significance influence on the practices of prevention and control of infection. Significance contributing factors effect such as access of IPC resources (β = 0.31), individual health worker practices on IPC (β = 0.31) and practices of IPC at the facility (β = 76). Practices of hand hygiene was found at (β = -0.45) which is the negative effect on adherence. CONCLUSION: Therefore, this study concluded that hand hygiene; access of IPC resource and individual practices on prevention and control of infection were the main factors influence poor adherence on IPC at Keetmanshoop hospital.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".