Mental ill-health during COVID-19 confinement
Bibliographic record
Abstract
BACKGROUND: Confinement due to COVID-19 has increased mental ill-health. Few studies unpack the risk and protective factors associated with mental ill-health and addictions that might inform future preparedness. METHODS: Cross-sectional on-line survey with 37,810 Catalan residents aged 16+ years from 21 April to 20 May 2020 reporting prevalence of mental ill-health and substance use and associated coping strategies and behaviours. RESULTS: Weighted prevalence of reported depression, anxiety and lack of mental well-being was, respectively, 23, 26, and 75%, each three-fold higher than before confinement. The use of prescribed hypnosedatives was two-fold and of non-prescribed hypnosedatives ten-fold higher than in 2018. Women, younger adults and students were considerably more likely, and older and retired people considerably less likely to report mental ill-health. High levels of social support, dedicating time to oneself, following a routine, and undertaking relaxing activities were associated with half the likelihood of reported mental ill-health. Worrying about problems living at home, the uncertainty of when normality would return, and job loss were associated with more than one and a half times the likelihood of mental ill-health. With the possible exception of moderately severe and severe depression, length of confinement had no association with reported mental ill-health. CONCLUSIONS: The trebling of psychiatric symptomatology might lead to either to under-identification of cases and treatment gap, or a saturation of mental health services if these are not matched with prevalence increases. Special attention is needed for the younger adult population. In the presence of potential new confinement, improved mental health literacy of evidence-based coping strategies and resilience building are urgently needed to mitigate mental ill-health.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".