Reliability and Validity of the Workplace Social Distance Scale
Bibliographic record
Abstract
Self-stigma, defined by a negative attitude toward oneself combined with the consciousness of being a target of prejudice, is a critical problem for psychiatric patients. Self-stigma studies among psychiatric patients have indicated that high stigma is predictive of detrimental effects such as the delay of treatment and decreases in social participation in patients, and levels of self-stigma should be statistically evaluated. In this study, we developed the Workplace Social Distance Scale (WSDS), rephrasing the eight items of the Japanese version of the Social Distance Scale (SDSJ) to apply to the work setting in Japan. We examined the reliability and validity of the WSDS among 83 psychiatric patients. Factor analysis extracted three factors from the scale items: "work relations," "shallow relationships," and "employment." These factors are similar to the assessment factors of the SDSJ. Cronbach's alpha coefficient for the WSDS was 0.753. The split-half reliability for the WSDS was 0.801, indicating significant correlations. In addition, the WSDS was significantly correlated with the SDSJ. These findings suggest that the WSDS represents an approximation of self-stigma in the workplace among psychiatric patients. Our study assessed the reliability and validity of the WSDS for measuring self-stigma in Japan. Future studies should investigate the reliability and validity of the scale in other countries.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".