Measuring intimate relationship self-stigma in serious mental illness: scale development and validation
Bibliographic record
Abstract
BACKGROUND: Individuals living with serious mental illness (SMI) face pervasive stigma, with damaging effects on their intimate lives. Although evidence suggests individuals might endorse stereotypes about their romantic relationships, no tool has addressed self-stigma in this domain. AIMS: This study aimed to develop and validate the Intimate Relationship Self-Stigma Scale (IRSS), specifically, to determine its structural and construct validity, as well as the internal consistency and test-retest reliability. METHODS: This instrument was co-constructed with peer support workers living with SMI. Using a bipolar semantic differential format, the IRSS captures both self-stigmatizing beliefs and positive self-perceptions. Items were generated through focus groups and literature review. Participants with SMI (N = 150) completed the IRSS online, along with measures of internalized stigma, self-esteem, and romantic functioning. RESULTS: Exploratory factor analyses supported a four-factor structure-Disclosure Effects, Self-Attractiveness, Stability, and Intimacy Needs-explaining 48.6% of the variance. Subscales demonstrated satisfactory internal consistency (ω = .69-.87), test-retest reliability (ICC = .79-.93), and expected correlations with related constructs. CONCLUSION: The IRSS fills a critical gap in recovery-oriented mental health care by providing a comprehensive assessment of internalized stigma in romantic contexts while simultaneously capturing positive self-perceptions. Further validation, including confirmatory factor analyses and cross-cultural validity, is warranted.
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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 | 0.012 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".