Gender and Risk Factors for Suicide
Bibliographic record
Abstract
OBJECTIVE: It is unclear whether clinical and behavioral suicide risk factors, identified primarily among men, can be extended to women. We therefore explored sex differences in psychopathology and personality variants among suicide completers. METHOD: Using the psychological autopsy method, we compared personality variants and the prevalence of psychopathology as a function of sex among 351 consecutive suicides in a large, urban community. Psychiatric diagnoses were obtained using the Structured Clinical Interview for DSM-IV-TR Axis I Disorders and the Structured Clinical Interview for DSM-IV Axis II Personality Disorders, and measures of impulsive aggression, temperament, and character were administered. Subsequently, we carried out secondary analyses between male and female suicides matched 2:1 for age, current depression, and number of lifetime depressive episodes. The study was conducted from late 2000 to 2005. RESULTS: Females were less likely to meet criteria for current and lifetime alcohol abuse, but those who did were less likely than males to have concurrent depression. On average, females were less impulsive, yet similar proportions of males and females were highly impulsive and impulsivity was associated with alcohol abuse irrespective of gender. Females were more likely to meet criteria for lifetime anxiety disorders; these were associated with nonviolent suicide methods, irrespective of gender. CONCLUSIONS: Despite a lower prevalence among females, high levels of impulsivity and alcohol abuse appear to be valid risk factors for both sexes. Researchers should focus on females for the identification of other suicide mediators.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".