Quality of sleep and well-being of health workers in Najran, Saudi Arabia
Bibliographic record
Abstract
Background: Health care involves taking care of other peoples' lives. Professionals in the field of health care are expected to be at their best all the time because mistakes or errors could be costly and sometimes irreversible. Aim: This study assessed the quality of sleep and well-being of health workers in Najran city, Saudi Arabia. Materials and Methods: It was a cross-sectional study done among health workers from different hospitals within the kingdom of Najran, Saudi Arabia. The subjects were administered questionnaire that contained sections on demographic and clinical characteristics, sleep quality, and section relating to well-being. Results: One hundred and twenty-three health workers comprising 29 (23.6%) males and 94 (76.4%) females participated in this study. The majority of the workers 74 (60.2%) were nurses; a quarter were doctors while the remaining 13.6% accounted for other categories of health workers such as the pharmacist and laboratory technicians. Fifty-two (42.3%) of the workers were poor sleepers. Significantly (χ2 = 23.98, P = 0.000), majority of the subjects that were poor sleepers (84.6%) compared with the 42.3% of the good sleepers rated the last 12 months of their profession as a bit stressful or quite a bit stressful. Similarly, 46.2% of the workers that were poor sleepers significantly (χ2 = 24.69, P = 0.000) rated their ability to handle unexpected and difficult problems in their life as fair or poor compared with 14.1% of the good sleepers Conclusion: Health workers expressed some level of stress in their professional life, and a good proportion of the subjects were poor sleepers. There is, therefore, the need to establish a program within the health-care organization to address social, physical, and psychological well-being at work.
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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) | 0.001 | 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".