Predicting the trajectory of non‐suicidal self‐injury among adolescents
Bibliographic record
Abstract
BACKGROUND: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a high-risk period for self-injurious behavior. Thus, identifying predictors that shape the course of post-discharge NSSI may provide insights into ways to improve clinical outcomes. Accordingly, we used machine learning to identify the strongest predictors of NSSI trajectories drawn from a comprehensive clinical assessment. METHODS: The study included adolescents (N = 612; females n = 435; 71.1%) aged 13-19-years-old (M = 15.6, SD = 1.4) undergoing inpatient treatment. Youth were administered clinical interviews and symptom questionnaires at intake (baseline) and before termination. NSSI frequency was assessed at 1-, 3-, and 6-month follow-ups. Latent class growth analyses were used to group adolescents based on their pattern of NSSI across follow-ups. RESULTS: Three classes were identified: Low Stable (n = 83), Moderate Fluctuating (n = 260), and High Persistent (n = 269). Important predictors of the High Persistent class in our regularized regression models (LASSO) included baseline psychiatric symptoms and comorbidity, past-week suicidal ideation (SI) severity, lifetime average and worst-point SI intensity, and NSSI in the past 30 days (bs = 0.75-2.33). Only worst-point lifetime suicide ideation intensity was identified as a predictor of the Low Stable class (b = -8.82); no predictors of the Moderate Fluctuating class emerged. CONCLUSIONS: This study found a set of intake clinical variables that indicate which adolescents may experience persistent NSSI post-discharge. Accordingly, this may help identify youth that may benefit from additional monitoring and support post-hospitalization.
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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 | 0.004 |
| 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 | 0.001 | 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".