Hand Skin Problems: Prevalence and Risk Factors Among Nurses Working at Surgical Departments in Ministry of Health Hospitals
Bibliographic record
Abstract
Background: Hand skin problems are widespread problems among health care members especially nurses.Aims: to determine the prevalence and risk factors of hand skin problems among nurses who are working at surgical departments in Ministry of Health Hospitals. Design: Descriptive cross-sectional study. Setting: This study was carried out at three ministry health hospitals in Assiut government: Al-Eman, Al-Shamlaa and Ophthalmology Hospitals. Sample: A purposive sample of 200 nurses working in surgical and operating room departments at Assiut ministry of health hospitals. Tool: A structured self-administrator’s questionnaire, included demographic characteristic of nurses, history about hand skin problems and risk factors for hand skin problems. Results: More than one third of nurses working from 40-45 hours weekly. More than one quarter of nurses were having hand skin problems and 11.5 % were complaining from hand skin problems less than one year ago. Majority of nurses had a satisfactory level of knowledge about risk factors of hand skin problems. Repeated hand washing is most common risk factors of hand skin problem. Conclusion and Recommendations: About aquarter of nurses have hand skin problems and some of them complained from hand skin problems less than one year ago. Efforts to improve skin condition must focus on improving products and identifying any interactive effects between hand care products and glove materials and brands.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".