Brazilian translation and adaptation of theQuestionnaire D'Alimentation
Bibliographic record
Abstract
ABSTRACT: Purpose: to translate the French language instrument "Questionnaire D'Alimentation" to Brazilian Portuguese and culturally adapt it for adolescents. Methods: the translation was based on a protocol that consisted of translation to Brazilian Portuguese (by a professor with a Literary Arts degree and a Doctor of Dental Surgery, both fluent in French and Brazilian Portuguese); back-translation into French; revision by a Committee of specialists (two translators, one native and one sworn translator, and two university professors, one being a Doctor of Dental Surgery and a Speech Therapist) and cultural equivalence (pre-test). The version used in the pre-test consisted of 26 questions with five possible responses (5-Likert), distributed in five domains (Food-Mastication, Habits, Meats, Fruits and Vegetables). The pre-test and test-retest was performed with a sample of 20 adolescents (10 boys/10 girls) from public schools of Piracicaba (Brazil). At this stage, the alternative "I did not understand" was added to each question in order to identify those that were not understood. Test-retest reliability was assessed for each domain using intra-class correlation coefficients (ICCs). Results: in the pre-test, an excellent comprehension of the instrument was observed; in test-retest, ICCs ranged from 0.45 to 0.81 (moderate to excellent agreement). Conclusion: the Portuguese version of the Questionnaire D'Alimentationhas shown to be easy to understand by Brazilian adolescents and useful in the evaluation of the masticatory function and feeding or swallowing disorders that may affect food intake.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".