Psychometric properties of the functions and addictive features scales of the Ottawa Self-Injury Inventory: A preliminary investigation using a university sample.
Bibliographic record
Abstract
Nonsuicidal self-injury (NSSI) is an issue primarily of concern in adolescents and young adults. Thus far, no single NSSI self-report measure offers a fully comprehensive assessment of NSSI, particularly including measurement of both its functions and potential addictive features. The Ottawa Self-Injury Inventory (OSI) permits simultaneous assessment of both these characteristics; the current study examined the psychometric properties of this measure in a sample of 149 young adults in a university student sample (82.6% girls, Mage = 19.43 years). Exploratory factor analyses revealed 4 functions factors (internal emotion regulation, social influence, external emotion regulation, and sensation seeking) and a single addictive features factor. Convergent evidence for the functions factor scores was demonstrated through significant correlations with an existing measure of NSSI functions and indicators of psychological well-being, risky behaviors, and context and frequency of NSSI behaviors. Convergent evidence was also shown for the addictive features scores, through associations with NSSI frequency, feeling relieved following NSSI, and inability to resist NSSI urges. Additional comment is made regarding the potential for addictive features of NSSI to be both negatively and positively reinforcing. Results show preliminary psychometric support for the OSI as a valid and reliable assessment tool to be used in both research and clinical contexts. The OSI can provide important information for case formulation and treatment planning, given the comprehensive and all-inclusive nature of its assessment capacities.
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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 | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".