Functions of nonsuicidal self-injury and repeated nonsuicidal self-injury among adolescents: A moderating role of addictive features
Bibliographic record
Abstract
OBJECTIVE: The high prevalence and addictive features of nonsuicidal self-injury (NSSI) in adolescents have been documented, but the role of addictive features in the process from NSSI functions to behaviour remains unclear. The major aim of this study was to investigate the effect of addictive features on NSSI functions and the severity of repeated NSSI. METHODS: A total of 10,781 students from primary and middle schools in Chengdu and Karamay were invited to participate in the online cross-sectional survey, and 10,501 completed the survey. Two self-report questionnaires, the Ottawa Self-Injury Inventory (OSI) and the Adolescent Self-Harm Scale (ASHS), were used to collect data from all participants. RESULTS: Among the students, 23.45% and 6.64% reported having engaged in NSSI at least once or at least five times in the past year. Being a girl, being an only child, and being in a single-parent family were significantly associated with more severe NSSI. Addictive features have high value for predicting repeated NSSI. In addition to their significant independent/direct additive effects, addictive features mediated and moderated the relationship between NSSI functions and increased severity of NSSI in adolescents. DISCUSSION AND CONCLUSIONS: The findings suggest that addictive features play a critical role in the development of repeated NSSI in adolescents, which indicates that addiction models may partially explain the mechanism underlying increased severity of NSSI. This may enhance understanding of the reasons for repeated NSSI and inform interventions for repeated 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".