Relationship Between Severity and Length of Exposure to COVID-19 Parameters and Resulting Government Responses and the Suicide Crisis Syndrome (SCS)
Bibliographic record
Abstract
Abstract Objective: The COVID-19 pandemic has had a globally devastating psychosocial impact. A detailed understanding of the mental health implications of this worldwide crisis is critical for successful mitigation of and preparation for future pandemics. Using a large international sample, we investigated in the present study the relationship between multiple COVID-19 parameters (both disease characteristics and government responses) and the incidence of the suicide crisis syndrome (SCS), an acute negative affect state associated with near-term suicidal behavior. Methods: Data were collected from 5528 adults across 10 different countries in an anonymous web-based survey between June 2020 and January 2021. Results: Individuals scoring above the SCS cut-off lived in countries with higher peak daily cases and deaths during the first wave of the pandemic. Additionally, the longer participants had been exposed to markers of pandemic severity (eg, lockdowns), the more likely they were to screen positive for the SCS. Findings reflected both country-to-country comparisons and individual variation within the pooled sample. Conclusion: Both the pandemic itself and the government interventions utilized to contain the spread appear to be associated with suicide risk. Public policy should include efforts to mitigate the mental health impact of current and future global disasters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".