Psychometric Properties of Persian Version of the Ottawa Self-Injury Inventory in Hospitalized Patients
Bibliographic record
Abstract
Introduction: Non-Suicidal Self-Injury (NSSI) has high prevalence among the patients with psychiatric disorders. Self-Injury plays an important role in the treatment, prognosis and risk of suicide, which highlights the need for an appropriate tool to assess the nature and psychological functions of NSSI. Therefore, the aim of this study was to determine the psychometric properties of the Persian version of the Ottawa Self-Injury inventory (OSI) in hospitalized patients. Methods: The present study was a psychometric study based on analytical method. The research has been performed on 310 patients with non-suicidal self-injury who have been referred to Nekoei-Hedayati Hospital in Qom City. Sample group completed Persian version of the Ottawa Self-Injury inventory. Data were analyzed using SPSS-16 and Cronbach’s alpha coefficient, Content Validity Index (CVI) and Content Validity Ratio (CVR). Results: The results showed that content validity index in this study was more than 0.75 and content validity ratio for validity of questions was more than 0.79. The impact score of all items (Except for the tenth question) was higher than 1.5 that confirmed the face validity of inventory. The total Cronbach's alpha coefficient was calculated 0.71. Another result of this study was that 52% of patients reported at least one addictive characteristic. Conclusion: The finding showed that Persian version of Ottawa Self-Injury inventory has appropriate validity (face /content) and reliability in the inpatient population
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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 | 0.002 | 0.011 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".