Trouble sleeping and work-life balance in European workers in the COVID-19
Bibliographic record
Abstract
Framework: There are several consequences caused and/or intensified by the pandemic disease COVID-19 in the lives of citizens around the world. The containment measures to combat this pandemic have entailed changes at the professional, personal, and social levels as well as in people's physical and mental well-being. Faced with this atypical situation, sleep is likely to be affected. Objective: The aim of this study was to characterize the sleep profile of workers residing in Europe aged 50 years or older and to analyze their perception of changes in this pattern during the COVID-19 pandemic as well as the implications on work-life balance. Methods: Cross-sectional study using data from the Survey of Health, Ageing and Retirement (Wave 9). A sample of 65,318,138 workers from 27 countries in Europe was selected. Results: About a quarter of the respondents (24.5%) reported having sleep problems being mainly women who reported this most (30.7% vs19%). Regarding the number of working hours, regardless of whether they increased or decreased during the COVID-19 confinement, the proportion of people with sleep problems was always higher than those who did not report such a problem. Of those who reported sleep problems, the proportion increased in those with negative mental health symptoms (sad or depressed; anxious, nervous, or on edge) and those who reported financial difficulties (need to draw on savings). As for changes in sleep pattern, about a third of the individuals perceived a worsening of their sleep problems since the first wave observing here a higher proportion in men than in women (40.6% vs 28.2%). Working from home and experiencing feelings of insecurity also seem to be related to these worsening problems. Conclusions: The results suggest that sleep problems worsened during COVID-19 confinement in European workers and affected work-life balance.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".