Mental health conditions among the general population, healthcare workers and quarantined population during the coronavirus disease 2019 (COVID-19) pandemic
Bibliographic record
Abstract
This study sought to assess the differences in mental health conditions among the general population, quarantined population and healthcare workers during the COVID-19 outbreak in China. An online rapid assessment captured depressive and anxiety symptoms, and sleep quality data. A total of 2689 participants (n=374 general population, n=403 healthcare workers, n=1912 quarantined population) were included in the final statistical analysis. The proportion of individuals with mild and/or serious depression and anxiety were higher in the general population when compared to the quarantined population and healthcare workers (58.6% vs. 25.1%vs. 48.6%, P<0.001; 41.2% vs. 18.5% vs. 35.7%, P<0.001). The prevalence of sleep disturbance was higher among healthcare workers than the general population and quarantined population (29.8% vs. 24.1% vs. 22.7%, P=0.013). Logistic regression analysis showed that, perceived effect on daily life was associated with depression, anxiety and sleep disturbance in the general population, quarantined population and the healthcare workers. The general population had a greater risk of developing psychological problems. The healthcare workers suffered the poorest sleep quality. Future research must further explorethe targeted measures for the general population and healthcare workers while combating COVID-19.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".