Cultural and linguistic adaptation of Bangla version of vitiligo impact scale-22
Bibliographic record
Abstract
Background: Vitiligo is known to have a major psychosocial effect on sufferers. “Vitiligo impact scale 22 (VIS 22)” is a newly developed specific scale to measure the quality of life (QoL) in vitiligo patients. As a psychometric evaluation of the Bangla version of VIS-22 has not yet been in Bangladesh. This study aimed to develop a culturally adapted Bangla version of English VIS-22 in Bengali-speaking vitiligo patients of Bangladesh. Methods: The original English version of the VIS-22 questionnaire was translated into Bangla following established cross-cultural procedures as recommended. The adapted Bangla version of VIS-22; and the Bangla version of SF-36 were administered and VASI was calculated. Results: The scale demonstrated a good internal consistency (Cronbach’s alpha=0.83) and significant test-retest reliability (by Pearson correlation 0.98 and by paired sample statistics 0.073). I-CVI for each item was 1. Construct validity assessing factor analysis-principal component analysis with the distribution of varimax rotation of VIS-22 Bangla ranged from 0.14 to 0.88 and six components were extracted. Criterion validity was assessed by comparing it with the VASI score which revealed Pearson correlation between VASI score and VIS-22 score was .30 which signifies a positive relationship. Correlation between the domains of SF-36 and VIS-22 Bangla was found between 0.45 to 0.60 in Pearson’s measurement. Conclusions: The adapted Bangla version of VIS-22 appears to be an acceptable, reliable, and valid instrument for measuring the QoL in Bengali-speaking patients of vitiligo.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".