Suicide Cases in New Brunswick from April 2002 to May 2003: The Importance of Better Recognizing Substance and Mood Disorder Comorbidity
Bibliographic record
Abstract
OBJECTIVE: To investigate all suicide cases that occurred in New Brunswick in the 14 months spanning April 1, 2002, to May 31, 2003, to determine 6-month and lifetime prevalence rates of psychopathology in the deceased. METHOD: We used 2 psychological autopsy methods: direct proxy-based interviews and medical chart reviews, together with telephone contacts with informants. Consensus DSM-IV diagnoses were formulated by clinical panels on the basis of the Structured Clinical Interviews I and II for DSM-IV complemented by medical charts. RESULTS: Of the 109 suicide deaths identified by the coroner at the time of the study, we were able to investigate 102. At time of death, 65% of the suicide victims had a mood disorder, 59% had a substance-related disorder, and 42% had concurrent mood and substance-related disorders. The lifetime prevalence of substance-related disorders among these suicide victims was 66%. Finally, 52% of the suicide victims presented with a personality disorder; one-half of these were of the cluster B type. CONCLUSIONS: Although treatment of depression has frequently been recognized as the focal point of clinically based suicide-prevention efforts, our results underscore substance-related disorders as a key dimension of completed suicide. Suicide-prevention programs should be designed to address this problem more directly.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".