Psychometric Properties for Persian Version of the Ottawa Self-injury Inventory-functions Among Adolescents
Bibliographic record
Abstract
Objectives Nonsuicidal self-injury (NSSI) is an issue primarily of concern in adolescents and young adults. The Ottawa self-Injury inventory (OSI) is a self-report measure that offers a comprehensive assessment of NSSI, including the measurement of its functions and addictive features. Thus, this study evaluated the psychometric properties of the Ottawa self-Injury inventory-functions (OSI-F) for assessing NSSI for gifted adolescents. Methods In this correlational study, 350 gifted adolescents who were selected using the convenience sampling method, answered the OSI-F, the short version of the self-harm screening inventory for adolescents, the depression, anxiety and stress scale, and the psychological capital questionnaire. Results The results of confirmatory factor analysis in line with the results of other studies supported the factor structure consisting of four factors. The model showed significant correlations between factors (rs=0.55-0.75, p<0.001). The results of the confirmatory factor analysis also confirmed the factor structure of the addictive characteristics of self-injurious behaviors. All the items had significant path estimates (0.42 to 0.83). Cronbach’s alpha for factors of internal emotional regulation, social influence, external emotional regulation, and sensation seeking in the scale of self-injurious behavior functions was equal to 0.86, 0.87, 0.64, and 0.72, respectively, and for the scale of addictive characteristics was 0.87. Convergent validity of scales of functions and addictive characteristics of self-injurious behavior was obtained through the relationship with scores in scales of anxiety, depression, stress, and deliberate self-injurious behaviors, as well as psychological capital factors. Conclusion Results showed preliminary psychometric support for the OSI as a valid and reliable assessment tool to be used in both research and clinical contexts.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".