Fatigue and Vitamin D Status in Iranian Female Nurses
Bibliographic record
Abstract
INTRODUCTION: Given that nurses are among professions with frequent problems of fatigue, and given the nature of their profession that provides little exposure to sunlight and the subsequent deficiency of vitamin D, the present study examined the relation between fatigue and circulating vitamin D levels in female nurses working in Shahid Beheshti Hospital, Kashan, Iran in 2013. MATERIAL & METHODS: This cross-sectional study was conducted in 200 female nurses working in Shahid Beheshti Hospital. To measure fatigue, fatigue questionnaire containing 9 questions eliciting the subject's feeling in scales of 1 to 7, getting a possible score of 9 to 63, and Visual Analogue Scale in which nurses specified their fatigue in a band of zero to 10 were used. The 25-hydroxyvitamin D, which is the most important vitamin D metabolite, also was determined. The data was analyzed by SPSS-16. The Pearson's correlation of coefficients, t-test, and multiple regression analysis were used in this study. RESULTS: The mean fatigue score of nurses was 38.76±12.66 in questionnaire and 5.73±2.12 in Visual Analog Scale. The 89 per cent of nurses suffered from vitamin D deficiency, 9.5 percent of them had normal level and 1.5 per cent had toxicity level of vitamin D. There was a significant relationship between vitamin D level and fatigue scores (P<0.0001), and visual fatigue scores (P<0.0001). According to multivariate regression analysis, vitamin D level accounted for 13 per cent of the fatigue based on data on questionnaire and 18.6 per cent of fatigue according to Visual Analog Scale. CONCLUSION: High prevalence of fatigue among nurses could be attributed to vitamin D deficiency.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".