Sleep of Healthcare Workers During the COVID-19 Pandemic and the Role of Atypical Work Schedules: A Scoping Review
Bibliographic record
Abstract
The COVID-19 pandemic has negatively impacted the well-being of healthcare workers (HCWs). HCWs are highly exposed to shift work and their work schedules have been subject to increasing unpredictability since the start of the pandemic. This review aims to: (1) map the studies providing information about factors associated with sleep characteristics in HCWs working in the context of the COVID-19 pandemic during the first and second waves and (2) examine the state of the evidence base in terms of the availability of information on the influence of atypical work schedules. A literature search was performed in PubMed. Studies containing information about factors (demographic; psychological; occupational; COVID-19-specific; work schedule; lifestyle; medical; or other) associated with various sleep characteristics among HCWs working in the context of the COVID-19 pandemic were included. Particular attention was paid to the availability of information on the role of atypical work schedules on HCW sleep. Fifty-seven articles met the inclusion criteria. Most studies were reports of quantitative cross-sectional surveys using self-report measures. Associations between female sex, frontline HCW status, psychological factors, and poorer sleep were observed. Six studies included a measure of shift work in their analyses, 5 of which reported an association between shift work status and sleep. A wide range of factors were investigated, with female sex, frontline HCW status, and psychological factors repeatedly demonstrating associations with poorer sleep. Sleep was predominantly measured in terms of self-reported sleep quality or insomnia symptoms. Few studies investigated the influence of atypical work schedules on HCW sleep in the context of the COVID-19 pandemic. Research on this topic is lacking in terms of reliable and consistent measurements of sleep outcomes, longitudinal data, and knowledge about the influence of covariates such atypical work schedules, comorbidity, and medical history on HCW sleep.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".