Characteristics of nonsuicidal self-injury associated with suicidal ideation: evidence from a clinical sample of youth
Bibliographic record
Abstract
BACKGROUND: Nonsuicidal self-injury (NSSI) and suicidal ideation (SI) are both distressing and quite common, particularly in youth. Given the relationship between these two phenomena, it is crucial to learn how we can use information about NSSI to understand who is at greatest risk of suicidal thoughts. In this study, we investigated how characteristics of nonsuicidal self-injury related to SI among treatment-seeking adolescents and young adults. METHODS: Data were collected during routine program evaluation for a self-injury treatment program. Correlations between recent SI and NSSI characteristics were calculated for adolescent and young adult patients (N = 1502). RESULTS: Low severity methods of NSSI (e.g. banging) were more strongly associated with SI than high severity methods (e.g. breaking bones). SI was associated with intrapersonal (automatic) NSSI functions. SI was associated with some indices of NSSI severity, such as number of methods and urge for NSSI, but not with others, such as age of onset. CONCLUSIONS: This study provides a valuable opportunity to expand our knowledge of suicide risk factors beyond those that may apply broadly to self-injurers and to non-injurers (e.g., depression, substance use) to NSSI-related factors that might be specifically predictive of suicidal thoughts among self-injurers. Findings inform clinical risk assessment of self-injurious youth, a population at high risk of suicidal thoughts and behaviors, and provide further insight into the complex NSSI/suicide relationship.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".