Examining shift duration and sociodemographic influences on the well-being of healthcare professionals in the United Arab Emirates: a cross-sectional study
Bibliographic record
Abstract
Objective: Providing quality healthcare is an essential part of the healthcare system. The high workload and night work associated with healthcare providing may result in work-life imbalance among healthcare professionals (HCPs) and in degradation in the quality of care. Methods: This cross-sectional study aimed to assess the effects of sociodemographic characteristics and shift work on HCPs' well-being in four large hospitals in the United Arab Emirates using a validated questionnaire. Results: Responses from 526 participants indicated that 79.2% of them were under the age of 40, 70.2% were females and 50.2% were single or divorced. Responses indicated that many HCPs reported engaging in unhealthy behaviors such as consuming energy drinks (37.3%), smoking (14.2%) and taking stimulant pills (5.7%) with males being more susceptible to these practices compared to females. Results also showed that participants perceived their overall health rate, on the average, to be moderate with one third of participants indicating to be overweight. Moreover, results showed that many participants reported having blood pressure issues (16%), diabetes (8.6%) and/or heart diseases (2.7%), with females more prone to these diseases than males. Another important finding was that most respondents reported sleeping significantly less than the recommended duration and taking too long time to fall asleep. Finally, results revealed that HCPs on 12-h shifts indicated having greater satisfaction but tend to experience more exhaustion levels and worsened health indicators than those on 8-h shifts. Conclusion: HCPs work has adverse effects on their well-being especially when combined with working at night. Healthcare implications of the results as well as recommendations to improve the well-being of HCPs based on the findings are given at the end.
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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 | 0.001 |
| 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 | 0.000 |
| 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".