Conceptualizing Stigma: The Development of a Cross-Cultural Scale to Measure Stigma Related to Depression
Bibliographic record
Abstract
The development of psychometrically sound, quantitative, and emically-driven measures of stigma across cultures has been identified as a critical lacuna in the growing body of literature on mental illness related stigma. The present investigation addresses this gap by designing a multidimensional measure of stigma with four separate participant pools comprised of Asian-Canadian and European-Canadian undergraduate students. Study 1 (N = 33) generated 144 scale items. In Study 2, 11 students, four stigma researchers, six consumers of depression, and three culture brokers evaluated the items. Scale design and development was conducted in Study 3 (N = 729) and includes the final factor solution. Study 4 (N = 258) investigates cross-cultural differences in stigmatizing attitudes using the new measure, as well as the scale’s convergent validity. The results of an exploratory factor analysis revealed a four-factor solution that evidenced strong internal consistency, test-retest reliability, and convergent validity. The four factors were titled: Culture, Personal, Workplace and Family. This measure may be used to identify nuanced variations in the experience of mental illness stigma that are accounted for by culture. The strengths and limitations as well as directions for future research are discussed.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".