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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".