Translation, Cross-Cultural Adaptation, and Evaluation of Psychometric Properties of Cystic Fibrosis Stigma Scale
Bibliographic record
Abstract
Abstract Background: Stigma is present during the lifespan of individuals with cystic fibrosis (CF); consequently, instruments to assess this psychosocial aspect are necessary. Few instruments are validated and adapted to Brazilian Portuguese. We aimed to translate, cross-culturally adapt, and evaluate psychometric properties of the CF stigma scale. Methods: We conducted an exploratory study of cross-cultural adaptation involving translation, back translation, revision by an expert committee, and a pre-test. Psychometric properties (content validity, test-retest reliability, and convergent validity) were analyzed based on the adapted version of the scale and responded by 52 Brazilian individuals with CF older than 18 years. Results: Translation and cross-cultural adaptation obtained kappa indexes higher than 0.61 on the expert committee phase and between 0.48 and 0.72 on pre-test. The Brazilian version of CF stigma scale showed excellent psychometric properties: i) internal consistency, α = 0.836; ii) mean correlation between items and test-retest: r = 0.886, p < 0.0001; and iii) convergent validity, CF stigma scale correlated positively with anxiety scale and negatively with general and specific scores of quality of life in CF. Conclusion: CF stigma scale was adequately translated and cross-culturally adapted for the Brazilian population. Psychometric properties of the Brazilian version favor its use in future studies regarding stigma conducted with Brazilian individuals with CF.
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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 | 0.014 | 0.034 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".