Translation, cross-cultural adaptation and psychometric evaluation of the Brazilian version of the Cystic Fibrosis Knowledge Scale (CFKS)
Bibliographic record
Abstract
BACKGROUND: Information on the level of knowledge about cystic fibrosis (CF) among affected people and their families is still scarce. OBJECTIVE: This study aimed to translate, cross-culturally adapt and analyze the psychometric properties of the Brazilian version of Cystic Fibrosis Knowledge Scale (CFKS). MATERIALS AND METHODS: The translation and cross-cultural adaptation involved the stages of translation, synthesis of translations, reverse translation, synthesis of reverse translations, review by a multi-professional committee of experts and pre-testing. The reliability, viability, construct, predictive, concurrent and discriminant validity were investigated. RESULTS: The sample consisted of 40 individuals with cystic CF, 47 individuals with asthma, 242 healthcare workers and 81 students from the health area. The Brazilian version of the CFKS presented high internal consistency (α = 0.91), moderate floor and ceiling effects, without differences in the test-retest scores. An analysis of factorial exploration identified three dimensions. Confirmatory factor analysis led to an acceptable data-model fit. There was good predictive validity, with a difference in the scores among all the evaluated groups (p <0.001), as well as good discriminant validity since individuals with asthma had greater knowledge of asthma compared to CF (r = 0.401, p = 0.005; r2 = 0.162). However, there was no difference between the diagnosis time and knowledge about CF (r = -0.25, p = 0.11; r2 = 0.06), either between treatment adherence and knowledge about CF (r = -0.04, p = 0.77; r2 = 0.002). CONCLUSION: The Brazilian version of the CFKS indicated that the scale is able to provide valid, reliable and reproducible measures for evaluating the knowledge about 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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".