Exposure to potentially traumatic events in young Swiss men: associations with socio-demographics and mental health outcomes (alcohol use disorder, major depression and suicide attempts)
Bibliographic record
Abstract
Background and objective: The aims of this study were to estimate the lifetime and 12-month prevalence of exposure to potentially traumatic events (PTEs) in young men in Switzerland and to assess factors and mental health outcomes associated with such events.Method: Data were drawn from the Cohort Study on Substance Use Risk Factors (C-SURF), encompassing 5,223 young men. Exposure to PTEs was assessed using the Post-traumatic Diagnostic Scale (PDS), Trauma History Questionnaire (THQ) and Life Event Checklist (LEC).Results: Lifetime prevalence of PTEs was 59.4%, with 37.3% reporting multiple types of events. Twelve-month prevalence was 31.2%, with 12.7% reporting multiple types of events. Low education level of participants, high maternal education, family affluence below average, and not living with biological parents were associated with a higher risk of having experienced one or more PTEs in one’s lifetime. Low education level of participants and high maternal education were also related to exposure to one or more PTEs over the past 12 months. Logistic regression analyses demonstrated that PTE exposure was directly associated with all assessed mental health outcomes. The strongest relationship was found between exposure to multiple types of PTEs and suicide attempts (adjusted OR 4.9 [95% CI: 2.9–8.4]).Conclusions: These results indicate that having experienced one or multiple types of PTEs is common in Swiss young men. Efforts should be intensified to reduce exposure to PTEs and prevent and treat resulting problematic mental health outcomes in young adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".