The Relationship Between Sleep Health, Sleep Regularity, Optimism, and Well-Being With Self-Rated Health: A Study on Healthcare Professionals
Bibliographic record
Abstract
Background and Objective This study investigates the impact of sleep, well-being, and optimism on self-rated health among healthcare professionals in the United Arab Emirates (UAE).Methods A cross-sectional approach was employed, using Pearson correlation and linear regression to analyze the relationship between sleep, well-being, optimism, and self-rated health among health care professioansl in the UAE.Results The age range of the participants was between 20–65 years, and they were predominantly female (68.7%). Significant predictors of self-rated health included well-being (p < 0.001), optimism (p = 0.004), and sleep circadian regularity (p = 0.009), explaining 10% of the variance in self-rated health (R2 = 0.103). Among the participants, 84.1% worked in public hospitals, and 15.9% in private hospitals. Regarding body mass index, 43.9% were of normal weight, 4.8% underweight, 32.4% overweight, and 18.9% obese. Males reported higher well-being and sleep continuity scores than females.Conclusions The study highlights the critical role of sleep health, well-being, and optimism in shaping healthcare professionals’ self-rated health. These results suggest that improving these mental health factors can positively influence healthcare professionals’ self-rated health, potentially enhancing their performance and patient care quality. Further research is needed to identify additional determinants and to establish causal relationships through longitudinal studies.
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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 | 0.003 |
| 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 | 0.001 | 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".