Self-harming Behaviors and suicide probability in delinquent adolescent girls: The role of emotion dysregulation and modeling the self-harming of peers
Bibliographic record
Abstract
Background: Adolescents may be exposed to several mental health related difficulties due to lack of complete cognitive maturity. Objective: This study investigated the mediation of emotion dysregulation and modeling of peers in relation between self-harming behaviors and suicide probability in adolescent. Material& Methods: In a descriptive study, we investigated juvenile delinquents of Correction and Rehabilitation Center of Mashhad during 2021. A total of 148 individuals were selected and evaluated using the self-harm motivation scale, Ottawa self-harming inventory, regulation problems scale and peer self-harm modeling scale. Data were analyzed using structural equation modeling in SmartPLS-3 software. Results: The mean age of participants was 16.21 (standard deviation=2.42). There was significant direct effect of self-harming behaviors on suicide probability (β=0.86 , P= 0.001 ). The significant indirect effect of self-harming behaviors on suicide probability through emotional dysregulation (β= 0.36, P= 0.001 ) was stronger than indirect effect mediated by peers modeling (β=0.17 , P= 0.04 ). The model account for 87 % of total variance of suicide probability. Conclusion: Based on the findings of the study, it can be concluded that emotion dysregulation and peers modeling play a role in increasing suicide probability and should be considered in preventing harmful behaviors in 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".