Association between sleep disturbance and mental health of healthcare workers: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives Sleep disturbance and mental health are challenges for healthcare workers (HCWs). Especially during the COVID-19 pandemic, they experienced more severe sleep and mental health problems. However, the association between sleep disturbance and the mental health of HCWs is still controversial. This study aimed to systematically review the relationship by conducting a systematic review and meta-analysis. Method Two researchers retrieved the literature from Web of Science, PubMed, EMBASE, CINAHL, Psyclnfo, and Cochrane Library from the establishment of the databases until November 20, 2021. We used the New Castle-Ottawa Scale (NOS) and Agency for Healthcare Research and Quality (AHRQ) to evaluate the risk of bias in prospective research and cross-sectional research, respectively. The major exposure was HCWs’ sleep disturbance, and the major outcome was mental health. The correlation coefficients ( r ), regression coefficients (β) and odds ratios (OR) of the included studies were integrated. Result Fifty-nine studies were included for qualitative analysis, of which 30 studies could be combined and entered into quantitative analysis. There were 23 studies during the COVID-19 pandemic among the 59 included studies. The results of the meta-analysis showed that the correlation coefficient between sleep disturbance and mental health was 0.43 (95% CI: 0.39–0.47). HCWs with sleep disturbance had a 3.74 (95% CI: 2.76–5.07) times higher risk of mental health problems than those without sleep disturbance. The correlation coefficient during the COVID-19 epidemic was 0.45 (95% CI: 0.37–0.53), while it was 0.40 (95% CI: 0.36–0.44) during the non-epidemic period. Subgroup analysis compared the OR results in epidemic and non-epidemic periods of COVID-19, which were 4.48 (95% CI: 2.75–5.07) and 3.74 (95% CI: 2.74–7.32), respectively. Conclusion Sleep disturbance and mental health problems were positively correlated among HCWs. Particularly in the COVID-19 pandemic, more attention should be given to this issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".