Group Interpersonal Psychotherapy for Adolescents With Non-suicidal Self-injury: A Randomized Controlled Study
Bibliographic record
Abstract
Non-suicidal self-injury (NSSI) is a significant clinical concern in adolescents. The goal of this study was to evaluate the efficacy of group interpersonal psychotherapy (G-IPT) combined with treatment as usual (TAU) compared with TAU alone in treating adolescents with NSSI. A randomized controlled trial was conducted in a psychiatric outpatient clinic in Hubei Province, China, involving 52 adolescents 12 to 17 years of age diagnosed with NSSI. Participants were randomly assigned to either the G-IPT combined with TAU group (n=26), who received 12 additional G-IPT sessions, or the TAU-alone group (n=26). Outcomes were assessed using the Ottawa Self-Injury Inventory (OSI), 9-item Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Interpersonal Disturbances Scale (IDS), and Interpersonal Communication Scale (ICS) at pre-treatment [Time Zero (T0)], immediately after completion of G-IPT, or 12 weeks after baseline assessment in the TAU group [Time 1 (T1)], and 3 months after treatment [Time 2 (T2)]. Forty-eight participants completed all assessments. The primary outcomes included a reduction in NSSI frequency and an increase in participants' ability to resist NSSI. Results demonstrated reductions in the OSI item "NSSI in the last month" at T1 (P<0.001) and T2 (P=0.008), as well as significant improvements in the OSI item "Strength to resist NSSI" (P<0.001 at T1; P=0.001 at T2). Significant between-group and interaction effects were observed, indicating that G-IPT combined with TAU was more effective than TAU alone in reducing NSSI behaviors. These findings underscore the potential of G-IPT as an effective adjunct to TAU in clinical settings for adolescent NSSI intervention.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".