Self-harm subscale of the Schedule of Nonadaptive and Adaptive Personality (SNAP): Predicting suicide attempts over 8 years of follow-up
Bibliographic record
Abstract
Objective: We examined the predictive power of the self-harm subscale of the Schedule for Nonadaptive and Adaptive Personality (SNAP) to identify suicide attempters in the Collaborative Longitudinal Study of Personality Disorders (CLPS).\nMethod: The SNAP, a self-report personality inventory, was administered to 733 CLPS participants at baseline, of whom 701 (96%) had at least 6 months of follow-up data. Cox proportional hazards regression analyses were performed to examine the SNAP–self-harm subscale (SNAP- SH) in predicting the 129 suicide attempters over 8 years of follow-up. Possible moderators of prediction were examined, including borderline personality disorder, major depressive disorder (MDD), and substance use disorder. We also compared baseline administration of the SNAP-SH to subsequent administrations more proximal to the suicide attempt, and to a higher-order SNAP-negative temperament (SNAP-NT) subscale. Receiver operating characteristic analyses were conducted using suicide attempts (n = 58) over the first year of follow-up to provide reference points for sensitivity and specificity.\nResults: The SNAP-SH demonstrated good predictive power for suicide attempts (hazard ratio = 1.28, P < .001) and appeared relatively consistent across borderline personality disorder, MDD, and substance use disorder diagnoses. Using more proximal scores did not increase predictive power. The SNAP-SH compared favorably to the predictive power of the higher-order SNAP-NT. Receiver operating characteristic analyses indicate several cutoff scores on the SNAP-SH that yield moderate to high sensitivity and specificity for predicting suicide attempts over the first year of follow-up.\nConclusions: The SNAP-SH may be a useful screening instrument for risk of suicide attempts in nonpsychotic psychiatric patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".