Translation, cross-cultural adaptation, and validation of the Canadian Diabetes Risk Questionnaire for the Brazilian population
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to translate, cross-culturally adapt, and validate the Canadian Diabetes Risk Questionnaire for use in Brazilian Portuguese. METHODS: This is a Questionnaire validity study conducted at a private university. The Brazilian version of the Canadian Diabetes Risk Questionnaire was developed by means of the processes of translation, back-translation, committee review, and pretesting. Test-retest reliability was measured using the intraclass correlation coefficient and kappa coefficient. Internal consistency was measured using Cronbach's alpha. For construct validity, the total score of the Canadian Diabetes Risk Questionnaire was correlated with the Diabetes Knowledge Scale and the Diabetes Mellitus Risk Questionnaire. Ceiling and floor effects were also evaluated in the present study. RESULTS: For construct validity and floor and ceiling effect measurements, a total sample of 100 participants was used. For reliability, a subsample of 34 participants out of the total sample was used. We identified adequate values for reliability (kappa between 0.46-1.00 and ICC 0.96) and internal consistency (Cronbach's alpha 0.80). There were significant correlations between the Canadian Diabetes Risk Questionnaire and the Diabetes Mellitus Risk Questionnaire (rs=0.370, p<0.001), but not the Diabetes Knowledge Scale (rs= -0.162). No ceiling or floor effects were found. CONCLUSION: We concluded that in accordance with the best international recommendations, the Brazilian version of the Canadian Diabetes Risk Questionnaire has adequate psychometric properties.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".