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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".