The relationship between low income and household food expenditure patterns in Canada
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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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 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".