Development and Validation of the Self-Injury Stigma Questionnaire (SIS-Q)
Bibliographic record
Abstract
Objectives : Nonsuicidal self-injury is a behavior typically used to regulate emotions, resulting in damage to one’s body without suicidal intent. Self-injury may indicate a need for psychological support, yet stigma is a major barrier to disclosure for many. Despite stigma’s potential impact on help-seeking, recovery, and wellbeing, our understanding of self-injury stigma is limited. While traction is building in this space, few measures of self-injury stigma exist to allow exploration of its impacts. In this study, we outline the preliminary validation of the Self-Injury Stigma Questionnaire (SIS-Q), which was developed to capture the features of self-injury stigma conceptualised in the NSSI Stigma Framework (Staniland et al., 2021). Method : First, we developed item pools representing five stigma perspectives: Public, Personal, Internalized, Anticipated, and Enacted, and reflecting six dimensions of stigma: Origin, Concealability, Aesthetics, Course, Peril, and Disruptiveness. These items were piloted with a sample of 316 ( M age = 32.1, 68% male, 40% with a history of self-injury) participants. Following item reduction, the scales were administered to 722 ( M age = 29.2, 27.3% male, 55.7% with a history of self-injury) participants, and data were analysed for validity, reliability, and invariance. Results : Confirmatory factor analyses revealed four factors: Origin, Concealability, Peril, and Disruption. Internal consistency, convergent and divergent validity, and measurement invariance were demonstrated. Conclusion : The SIS-Q offers a comprehensive and theoretically informed measure to support ongoing efforts investigating the development and persistence of self-injury stigma, and the impact it has on individuals with lived experience of self-injury.
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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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".