<i>Cultural Relevance of a Fruit and Vegetable</i> Food Frequency Questionnaire
Bibliographic record
Abstract
PURPOSE: Canada's multicultural population poses challenges for culturally competent nutrition research and practice. In this qualitative study, the cultural relevance of a widely used semi-quantitative fruit and vegetable food frequency questionnaire (FFQ) was examined among convenience samples of adults from Toronto's Cantonese-, Mandarin-, Portuguese-, and Vietnamese-speaking communities. METHODS: Eighty-nine participants were recruited through community-based organizations, programs, and advertisements to participate in semi-structured interviews moderated in their native language. Data from the interviews were translated into English and transcribed for analysis using the constant comparative approach. RESULTS: Four main themes emerged from the analysis: the cultural relevance of the foods listed on the FFQ, words with multiple meanings, the need for culturally appropriate portion-size prompts, and the telephone survey as a Western concept. CONCLUSIONS: This research highlights the importance of investing resources to develop culturally relevant dietary assessment tools that ensure dietary assessment accuracy and, more important, reduce ethnocentric biases in food and nutrition research and practice. The transferability of findings must be established through further research.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".