Anxiety and Depression among the Nursing Staff, Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Anxiety and depression are the most common mental problems that may affect workers' productivity. Diagnosing these disorders and determining their predisposing factors helps improving quality and productivity of workers; particularly nurses, with a positive impact on quality of service provided and as a preventive measure would save the cost of management of such disorders. PARTICIPANTS & METHODS: This questionnaire-based cross-sectional study was conducted between December 2021 and March 2022 and anonymously and voluntarily invited 250 nurses. Data were collected included the socio-demographic, anthropometric and life style data of participants and items of hospital anxiety and depression scale questionnaire. SPSS was used for data analysis that were presented as mean ± SD and frequencies; number (n) and percentage (%). RESULTS: Consented 215 nurses were enrolled giving a response rate of 86%. The mean ± SD score of anxiety was 8.4 ± 3.9 and the mean ± SD score of depression was 6.6 ± 3.9 and varied significantly by gender. The prevalence of anxiety and depression was 28.8% and 16.7%, respectively. There was significant association between anxiety and hospital location, body mass index, physical activity and overtime work. The hospital location, South Asian ethnicity, smoking, physical activity and night shift were all showed significant association with depression. CONCLUSION: Our figures are much lower than some international and national studies but still are higher than others. However, they are alarming to the needs of changes to improve the quality of nurses' life so as to ensure better healthcare services that save both sides the emotional and economic burden.
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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 | 0.001 | 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".