Development of a Web-Based 24-h Dietary Recall for a French-Canadian Population
Bibliographic record
Abstract
Twenty-four-hour dietary recalls can provide high-quality dietary intake data, but are considered expensive, as they rely on trained professionals for both their administration and coding. The objective of this study was to develop an automated, self-administered web-based 24-h recall (R24W) for a French-Canadian population. The development of R24W was inspired by the United States Department of Agriculture (USDA) Automated Multiple-Pass Method. Questions about the context of meals/snacks were included. Toppings, sauces and spices frequently added to each food/dish were suggested systematically. A list of frequently forgotten food was also suggested. An interactive summary allows the respondent to track the progress of the questionnaire and to modify or remove food as needed. The R24W prototype was pre-tested for usability and functionality in a convenience sample of 29 subjects between the ages of 23 and 65 years, who had to complete one recall, as well as a satisfaction questionnaire. R24W includes a list of 2865 food items, distributed into 16 categories and 98 subcategories. A total of 687 recipes were created for mixed dishes, including 336 ethnic recipes. Pictures of food items illustrate up to eight servings per food item. The pre-test demonstrated that R24W is easy to complete and to understand. This new dietary assessment tool is a simple and inexpensive tool that will facilitate diet assessment of individuals in large-scale studies, but validation studies are needed prior to the utilization of the R24W.
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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".