Development and use of FFQ among adults in diverse settings across the globe
Bibliographic record
Abstract
In nutritional epidemiology, development of valid dietary assessment instruments specific to populations in diverse settings is of paramount importance. Such instruments are essential when trying to characterise dietary patterns and intake, investigate diet-disease associations, inform and evaluate nutrition interventions, assess nutrient-gene interactions, conduct cross-country comparison studies and monitor nutrition transitions. The FFQ is a relatively inexpensive tool for measuring long-term dietary intake for large populations and for allowing researchers to track dietary changes over time. However, FFQ must be population specific to capture the local diet and available foods. Collecting 24-h dietary recalls and utilising community feedback to build the FFQ ensures that a culturally appropriate instrument is developed. This article presents several examples describing FFQ development and utilisation in different settings globally. In the Canadian Arctic, FFQ were developed and utilised to inform and evaluate a community-based intervention programme, characterise the diet and track dietary changes occurring among Inuit and Inuvialuit, populations experiencing rising rates of chronic disease and likely to be extremely vulnerable to the potential effects of climate change. Another example is an FFQ developed to assess sodium intake and evaluate a sodium reduction trial in a high-risk population in Barbados. An example is provided from Brazil, where an FFQ was developed to assess associations between diet, heterocyclic aromatic amines and colorectal adenoma among Japanese Brazilians and to conduct cross-country comparisons. These and other case studies highlight the diversity in dietary intake between populations and the need for FFQ to be developed to capture this diversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".