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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".