Influencing factors of sleep disorders and sleep quality in healthcare workers during the <scp>COVID</scp>‐19 pandemic: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: The aim of this study was to identify the influencing factors of sleep disorders and sleep quality in healthcare workers during the COVID-19 pandemic. DESIGN: Systematic review and meta-analysis of observational research. METHODS: The databases of the Cochrane Library, Web of Science, PubMed, Embase, SinoMed database, CNKI, Wanfang Data, and VIP were systematically searched. The quality of studies was assessed using the Agency for Healthcare Research and Quality evaluation criteria and the Newcastle-Ottawa scale. RESULTS: A total of 29 studies were included, of which 20 were cross-sectional studies, eight were cohort studies, and 1 was a case-control study; 17 influencing factors were finally identified. Greater risk of sleep disturbance was associated with female gender, single relationship status, chronic disease, insomnia history, less exercise, lack of social support, frontline work, days served in frontline work, department of service, night shift, years of work experience, anxiety, depression, stress, received psychological assistance, worried about being infected, and degree of fear with COVID-19. CONCLUSIONS: During the COVID-19 pandemic, healthcare workers did have worse sleep quality than the general population. The influencing factors of sleep disorders and sleep quality in healthcare workers are multifaceted. Identification and timely intervention of resolvable influencing factors are particularly important for preventing sleep disorders and improving sleep. PATIENT OR PUBLIC CONTRIBUTION: This is a meta-analysis of previously published studies so there was no patient or public contribution.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".