A multivariate study of predictors of repeat parasuicide
Bibliographic record
Abstract
OBJECTIVE: To identify variables which differentiate future repeaters of parasuicide from non-repeaters in a multivariate analysis. METHOD: Interviews were conducted with 507 parasuicide cases; data were collected on precipitating factors for the index parasuicide, psychiatric and medical history, stressful life events, prior history of parasuicide, hopelessness, anger, self-esteem and social adjustment. Individuals were followed for 1-2 years to determine if a repeat parasuicide occurred. RESULTS: A logistic regression model identified four significant predictors of repeat parasuicide: prior history of parasuicide, a history of depression, a history of schizophrenia and poor physical health. A risk factor scale constructed from these four variables showed that the risk of repeat parasuicide increases as the number of risk factors increases. CONCLUSION: This study identifies four key predictors of repeat parasuicide, and provides evidence that the risk of repeat parasuicide increases when multiple risk factors are present.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".