Personality traits as correlates of suicide attempts and suicidal ideation in young adults
Bibliographic record
Abstract
BACKGROUND: Adults in their twenties appear to be at high risk for suicidal behaviors (SBs) and there is substantial evidence suggesting that certain personality traits may increase individual vulnerability to suicide. METHOD: We investigated relationships of personality traits with two SBs in a cohort (n=1140) of 21- to 24-year-old adults, representative of the general population of Quebec. Subjects were assessed using a series of structured diagnostic and personality trait questionnaires. Multivariate logistic regression analyses were employed to identify personality trait correlates of suicide-attempt history and serious suicidal ideation in the context of other known risk factors, such as psychopathology and experiences of childhood sexual and physical abuse. RESULTS: Traits of conduct problems contributed to both suicide attempts [odds ratio (OR) 1.03, 95% confidence interval (CI) 1.01-1.06] and suicidal ideation (OR 1.04, 95% CI 1.02-1.07), while identity problems (OR 1.10, 95% CI 1.07-1.13) and gender-moderated impulsivity contributed exclusively to suicidal ideation. CONCLUSIONS: Personality traits may make independent contributions to current suicidal ideation and previous suicide attempts in certain subgroups of suicidal individuals. In order to further explore their utility as markers of suicide risk and targets of intervention further investigation in clinical samples and other cultural and age groups is necessary.
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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 | 0.000 | 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) | 0.001 | 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".