Nutritional assessment of charitable meal programmes serving homeless people in Toronto
Bibliographic record
Abstract
OBJECTIVES: To assess the potential nutritional contribution of meals provided in a sample of community programmes for homeless individuals, to determine the effect of food donations on meal quality and to develop food-based guidance for meals that would meet adults' total nutrient needs. SETTING: Toronto, Canada. DESIGN: An analysis of weighed meal records from eighteen programmes. The energy and nutrient contents of meals were compared to requirement estimates to assess contribution to total needs, given that homeless people have limited access to nutritious foods. Mixed linear modelling was applied to determine the relationship between the use of food donations and meal quality. The composition of meals that would meet adults' nutrient requirements was determined by constructing simulated meals, drawing on the selection of foods available to programmes. SAMPLE: In all, seventy meals, sampled from eighteen programmes serving homeless individuals. RESULTS: On average, the meals contained 2.6 servings of grain products, 1.7 servings of meat and alternatives, 4.1 servings of vegetables and fruits and 0.4 servings of milk products. The energy and nutrient contents of most meals were below adults' average daily requirements. Most meals included both purchased and donated foods; the vitamin C content of meals was positively associated with the percentage of energy from donations. Increasing portion sizes improved the nutrient contribution of meals, but the provision of more milk products and fruits and vegetables was required to meet adults' nutrient requirements. CONCLUSIONS: The meals assessed were inadequate to meet adults' nutrient requirements. Improving the nutritional quality of meals requires additional resources.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".