Self-Stigma in Relatives of people with Mental Illness scale: development and validation
Bibliographic record
Abstract
BACKGROUND: Serious mental illness (SMI) is profoundly stigmatised, such that there is even an impact on relatives of people with SMI. Aims To develop and validate a scale to comprehensively measure self-stigma among first-degree relatives of individuals with SMI. METHOD: We conducted group interviews focusing on self-stigma with first-degree relatives (n = 20) of people with SMI, from which 74 representative quotations were reframed as Likert-type items. Cognitive interviews with relatives (n = 11) identified 30 items for the Self-Stigma in Relatives of people with Mental Illness (SSRMI) scale. Relatives (n = 195) completed the scale twice, a month apart, together with four external correlate scales. RESULTS: The 30-item SSRMI was reliable, with scores stable over time. Its single-factor structure allowed generation of a 10-item version. Construct validity of 30- and 10-item versions was supported by expected relationships with external correlates. CONCLUSIONS: Both versions of the SSRMI scale are valid and reliable instruments appropriate for use in clinical and research contexts. Declaration of interest None.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".