Improving the nutritional status of food-insecure women: first, let them eat what they like
Bibliographic record
Abstract
OBJECTIVE: To determine the extent to which identified nutrient inadequacies in the dietary intakes of a sample of food-insecure women could be ameliorated by increasing their access to the 'healthy' foods they typically eat. DESIGN: Merged datasets of 226 food-insecure women who provided at least three 24-hour dietary intake recalls over the course of a month. Dietary modelling, with energy adjustment for severe food insecurity, explored the effect of adding a serving of the woman's own, and the group's typically chosen, nutrient-rich foods on the estimated prevalence of nutrient inadequacy. SETTING AND SUBJECTS: One study included participants residing in 22 diverse community clusters from the Atlantic Provinces of Canada, and the second study included food bank attendees in Toronto, Ontario, Canada. Of the 226 participants, 78% lived alone with their children. RESULTS: While nutritional vulnerability remained after modelling, adding a single serving of either typically chosen 'healthy' foods from women's own diets or healthy food choices normative to the population reduced the prevalence of inadequacy by at least half for most nutrients. Correction for energy deficits resulting from severe food insecurity contributed a mean additional 20% improvement in nutrient intakes. CONCLUSIONS: Food-insecure women would sustain substantive nutritional gains if they had greater access to their personal healthy food preferences and if the dietary compromises associated with severe food insecurity were abated. Increased resources to access such choices should be a priority.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".