Self‐injury motives: A person‐centered examination
Bibliographic record
Abstract
INTRODUCTION: People report multiple motives for nonsuicidal self-injury (NSSI), but few studies have examined how these motives relate to one another. This study identified person-centered classes of NSSI motives, their NSSI and psychopathological correlates, and their utility in predicting future NSSI across two samples. METHODS: Participants were adolescents and young adults (aged 15-35) with recent NSSI recruited from online forums (n = 155, Sample 1) or the community (n = 127, Sample 2). Participants completed measures of NSSI, emotion regulation difficulties, borderline personality disorder (BPD), depression, and reported on their NSSI over 12 months. RESULTS: Latent profile analyses yielded five classes in each sample: low interpersonal, self-punishment/interpersonal, moderate intra/interpersonal, high intra/interpersonal, and mainly interpersonal motives. Classes were not associated with lifetime NSSI characteristics, but highly motivated participants reported more severe depression and BPD symptoms, and greater emotion dysregulation than low-motivated participants. Those in the mainly interpersonal (Sample 1) and self-punishment/interpersonal (Sample 2) motives classes reported greater NSSI frequency during follow-up. CONCLUSIONS: This study identified five classes of NSSI motives. Participants who report multiple motives for NSSI may be more clinically severe, whereas those who report strong desires to communicate with others or punish themselves may be at the highest risk for more frequent NSSI over time.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".