Translation and Cultural Adaptation of the Short-Form Food Frequency Questionnaire for Pregnancy into Brazilian Portuguese
Bibliographic record
Abstract
OBJECTIVE: To translate and culturally adapt the short-form Food Frequency Questionnaire (SFFFQ) for pregnant women, which contains 24 questions, into Brazilian Portuguese. METHODS: Description of the process of translation and cultural adaptation of the SFFFQ into Brazilian Portuguese. The present study followed the recommendation of the International Society for Pharmacoeconomics and Outcomes Research for translation and cultural adaptation with the following steps: 1) preparation; 2) first translation; 3) reconciliation; 4) back translation; 5) revision of back translation; 6) harmonization; 7) cognitive debriefing; 8) revision of debriefing results; 9) syntax and orthographic revision; and 10) final report. Five obstetricians, five dietitians and five pregnant women were interviewed to contribute with the language content of the SFFFQ. RESULTS: Few changes were made to the SFFFQ compared with the original version. These changes were discussed with the research team, and differences in language were adapted to suit all regions of Brazil. CONCLUSION: The SFFFQ translated to Brazilian Portuguese can now be validated for use in the Brazilian population.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".