Why Stop Self-Injuring? Development of the Reasons to Stop Self-Injury Questionnaire
Bibliographic record
Abstract
We developed a measure of reasons to refrain from nonsuicidal self-injury (NSSI), the Reasons to Stop Self-Injury Questionnaire (RSSIQ), and examined how such reasons are associated with vulnerability versus resiliency for NSSI. Following qualitative item generation, we explored the factor structure, reliability, and convergent validity of the RSSIQ in 218 self-injuring undergraduates. In Study 2, we confirmed the hierarchical factor structure in 146 self-injuring individuals. In Study 3, we examined the incremental predictive validity of the RSSIQ. These studies resulted in a 40-item inventory with nine subscales and two higher-order factors. Resiliency-related reasons to stop NSSI were associated with greater hopefulness, social support, and adaptive coping, and prospectively protected against NSSI 3 months later, while vulnerability-related reasons were associated with greater psychopathology and dysfunctional coping, and predicted more chronic and severe NSSI. These studies, and the RSSIQ, can enhance the assessment and treatment of NSSI by clarifying motivations to stop NSSI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".