The Cultural Validation of Two Scales assessing Albinism - related Social Stigma among High School Students in Tanzania
Bibliographic record
Abstract
Purpose: Albinism in Tanzania causes fierce stigmatisation. Although various stigma reduction interventions (SRI) are employed, research on their effectiveness is lacking. This research aimed to develop a tool to measure albinism-related social stigma among high school students in Tanzania. Cultural equivalence was tested for the Explanatory Model Interview Catalogue Community Stigma Scale (EMIC-CSS) and Albinism Social Distance Scale for Adolescents (ASDS-A) assessing conceptual, item, semantic, operational and measurement equivalence. Methods: The methods used were workshops, in-depth interviews, translation and re-translation, discussions, a test (n=337) re-test (n=142) of the survey, and follow-up focus group discussions (n=25). Results: The Scales have proven to be adequate on all equivalences other than measurement equivalence. The reproducibility statistics raise questions that can be explained by characteristics of the sample. Conclusion and Implications: The analysis provides insights for further validation of the Scales, contributes to the discussion about a universal stigma measurement tool and demonstrates the importance of validation studies of existing and proven tools used in a different context.
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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 | 0.007 | 0.014 |
| 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 | 0.001 | 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".