The relationship between low income and household food expenditure patterns in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To compare food expenditure patterns between low-income households and higher- income households in the Canadian population, and to examine the relationship between food expenditure patterns and the presence or absence of housing payments among low-income households. DESIGN: Secondary data analysis of the 1996 Family Food Expenditure Survey conducted by Statistics Canada. SETTING: Sociodemographic data and 1-week food expenditure data for 9793 households were analysed. SUBJECTS: Data were collected from a nationally representative sample drawn through stratified multistage sampling. Low-income households were identified using Statistics Canada's Low Income Measures. RESULTS: Total food expenditures, expenditures at stores and expenditures in restaurants were lower among low-income households compared with other households. Despite allocating a slightly greater proportion of their food dollars to milk products, low-income households purchased significantly fewer servings of these foods. They also purchased fewer servings of fruits and vegetables than did higher-income households. The effect of low income on milk product purchases persisted when the sample was stratified by education and expenditure patterns were examined in relation to income within strata. Among low-income households, the purchase of milk products and meat and alternatives was significantly lower for households that had to pay rents or mortgages than for those without housing payments. CONCLUSIONS: Our findings indicate that, among Canadian households, access to milk products and fruits and vegetables may be constrained in the context of low incomes. This study highlights the need for greater attention to the affordability of nutritious foods for low-income groups.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it