Assessment of Nurses' Exposure to Chronic Diseases in Thi-Qar Governorate Hospitals
Bibliographic record
Abstract
Objective: To assess nurses' exposure to hospitals chronic diseases hazards in Thi-Qar governorate, and to identify the association between nurses' socio-demographic characteristics of age, sex, marital status, place of work, the experience and educational attainment and their exposure to the hazards of chronic diseases. Methodology: A purposive "non-probability" sample of (433) nurses who were selected from four public hospitals in Thi-qar governorate for the period from November 4th 2013 to June 8th of 2014. Results: The study results indicated that that the vast majority of participants have mild chronic diseases and health problems (86.8%), nurses' age and years of working in nursing negatively correlate with occurrence of chronic disease, more than half of them are within 20-29 years-old, less than half of them have ≤ 5 years of working in nursing (45%), more than quarter of them work in emergency room, and less than half of those who have mild chronic diseases are preparatory nursing schools graduate (44.7%). Recommendations: Initiating a training program; especially; for newly working nurses that aim to prevent the occurrence of chronic diseases, Increase public awareness and education for Nurses workers in hospitals through posters, seminars, and medi
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".