Mental health responses to COVID-19 around the world
Bibliographic record
Abstract
Background: The mental health impact of the COVID-19 crisis may differ from previously studied stressful events in terms of psychological reactions, specific risk factors, and symptom severity across geographic regions worldwide.Objective: To assess the impact of COVID-19 on a wide range of mental health symptoms, to identify relevant risk factors, to identify the effect of COVID-19 country impact on mental health, and to evaluate regional differences in psychological responses to COVID-19 compared to other stressful events.Method: 7034 respondents (74% female) participated in the worldwide Global Psychotrauma Screen – Cross-Cultural responses to COVID-19 study (GPS-CCC), reporting on mental health symptoms related to COVID-19 (n = 1838) or other stressful events (n = 5196) from April to November 2020.Results: Events related to COVID-19 were associated with more mental health symptoms compared to other stressful events, especially symptoms of PTSD, anxiety, depression, insomnia, and dissociation. Lack of social support, psychiatric history, childhood trauma, additional stressful events in the past month, and low resilience predicted more mental health problems for COVID-19 and other stressful events. Higher COVID-19 country impact was associated with increased mental health impact of both COVID-19 and other stressful events. Analysis of differences across geographic regions revealed that in Latin America more mental health symptoms were reported for COVID-19 related events versus other stressful events, while the opposite pattern was seen in North America.Conclusions: The mental health impact of COVID-19-related stressors covers a wide range of symptoms and is more severe than that of other stressful events. This difference was especially apparent in Latin America. The findings underscore the need for global screening for a wide range of mental health problems as part of a public health approach, allowing for targeted prevention and intervention programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".