Increased risk for mental disorders and suicide during the COVID-19 pandemic: the position statement of the Section on Suicidology and Suicide Prevention of the European Psychiatric Association.
Bibliographic record
Abstract
In March 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic. The Section on Suicidology and Suicide Prevention of the European Psychiatric Association (EPA) wants to raise awareness about the potential increase in mental health disorders and suicides as a result of the socio-economic impact of the COVID-19 pandemic, and of the necessary restrictive measures adopted worldwide to contain its spread. Even if fear, worries and symptoms of anxiety, depression and stress can be considered a natural response to this global crisis, some individuals are overexposed to its potential negative effects, such as healthcare workers, COVID-19 and psychiatric patients, prisoners, members of the LGBTQ+ community, migrants (including migrant workers), ethnic minorities and asylum seekers and internally displaced populations. Nevertheless, social support, resilience, a supportive work environment and other protective factors may buffer the impact of this crisis on mental health. These unprecedented times are calling for unprecedented efforts. Evidence-based and coordinated actions to prevent the risk of increased mental health disorders and suicide are needed. However, most of the data about COVID-19 impact on mental health comes from online surveys using non-probability and convenience sample in which females are often over-represented. For this reason the quality of future research should be also improved.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".