Profile of suicide attempts and risk factors among psychiatric patients: A case-control study
Bibliographic record
Abstract
BACKGROUND: Suicidal behaviour remains challenging for clinicians to predict, with few established risk factors and warning signs among psychiatric patients. AIM: We aimed to describe characteristics and identify risk factors for suicide attempts among patients with psychiatric disorders. METHODS: Multivariable logistic regression analysis, adjusted for clinically important confounders, was employed to determine risk factors for suicide attempts within a psychiatric patient population. RESULTS: The case (n = 146) and control groups (n = 104) did not differ significantly with regards to sociodemographic characteristics. The majority of the participants who had attempted suicide did so with high intent to die, and expected to die without medical intervention. The primary method of attempt was pharmaceutical overdose among the case participants (73.3%). Results showed impulsivity (odds ratio [OR] = 1.15, 95% confidence interval [CI] = 1.03-1.30) and borderline personality symptoms (OR = 1.07, 95% CI = 1.01-1.13) were significantly associated with attempted suicide. CONCLUSIONS: Our findings indicate that known sociodemographic risk factors for suicide may not apply within psychiatric populations. Prevention strategies for suicidal behaviour in psychiatric patients may be effective, including limited access to means for suicide attempts (i.e. excess pharmaceutical drugs) and target screening for high-risk personality and impulsivity traits.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".