Food Purchasing and Food Insecurity: Among Low-income Families in Toronto
Bibliographic record
Abstract
PURPOSE: Factors underlying food-purchasing decisions were examined among a sample of low-income Toronto families. METHODS: A cross-sectional survey was completed among 485 families residing in high-poverty Toronto neighbourhoods. Food-security status was assessed using the Household Food Security Survey Module. Open-ended questions were included to examine respondents' food selection and management practices and their purchasing decisions for six indicator foods. Logistic regression was used to examine associations between factors influencing food-purchasing decisions, perceived food adequacy, and severity of food insecurity. RESULTS: Twenty-two percent of families had been severely food insecure in the past 30 days. Respondents engaged in thrifty food shopping practices, such as frequenting discount supermarkets and budgeting carefully. Price was the most salient factor influencing food-purchasing decisions; the likelihood that families would report this factor increased with deteriorating food security. Preference, quality, and health considerations also guided food-purchasing decisions, but generally to a lesser extent as food insecurity increased. Household food supplies reflected constraints on food purchasing, and they diminished with increasing food insecurity. CONCLUSIONS: Despite their resourcefulness, low-income families struggle to feed their families. Dietitians have an important role to play as advocates for adequate income supports to promote food security and nutritional health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".