Clinical characteristics and functions of non-suicide self-injury in youth
Bibliographic record
Abstract
PURPOSE: Little is known about the clinical characteristics and motivations for engaging in non-suicide self-injury (NSSI) behaviors in adolescence. The aim of this study was to examine the prevalence, characteristics and functions of NSSI among adolescents in community settings, and to explore risk factors related to this behavior. SUBJECTS AND METHODS: Two hundred and seventy-five adolescents aged 12 to 17 were recruited randomly from different High Schools in Israel. They completed self-report questionnaires assessing NSSI (Ottawa Self-Injury Inventory), depression (Children's Depression Inventory - CDI) and impulsivity (Barratt Impulsiveness Scale - BIS-II). RESULTS: In the past year, 20.7% of the participants reported engaging NSSI at least once. Among them, 42.1% declared they are still engaging in NSSI at the present. Motives for NSSI were internal emotion regulation reasons, external emotion regulation reasons for social influences. In addition, the NSSI group reported significantly higher levels of depressive, impulsivity and suicidal ideations. Depressive symptoms were found as significant predictors of NSSI in the future. DISCUSSION AND CONCLUSIONS: High rates of NSSI among community adolescents were found. Depression, impulsivity and suicidal ideation were found significantly related to NSSI. Mental health professionals in schools and in primary care should routinely assess NSSI among adolescents.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".