Development of an optimal grocery list based on actual intake from a cross-sectional study of First Nations adults in Ontario, Canada
Bibliographic record
Abstract
A multi-stage sampling strategy selected 1387 on-reserve First Nations adults in Ontario. Foods from a 24-hour dietary recall were assigned to the 100 most common food groups for men and women. Nutrients from market foods (MF) and traditional foods (TF) harvested from the wild as well as MF costs were assigned based on the proportions of total grams consumed. Linear programming was performed imposing various constraints to determine whether it was possible to develop diets that included the most popular foods while meeting Institute of Medicine guidelines. Final models were obtained for both sexes with the top 100 food groups consumed while limiting the nutrient-poor foods to no more than the actual observed intake. These models met all nutrient constraints for men but those for dietary fibre, linoleic acid, phosphorus, and potassium were removed for women. MF costs were obtained from community retailers and online resources. A grocery list was then developed and MF were costed for a family of 4. The grocery list underestimated the actual weekly food cost because TF was not included. Contemporary observed diets deviated from healthier historic First Nations diets. A culturally appropriate diet would include more traditional First Nations foods and fewer MF. Novelty: Linear programming is a mathematical approach to evaluating the diets of First Nations. The grocery list is representative of food patterns within Ontario First Nations and can be used as an alternative to the nutritious food basket used for public health food costing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".