Socio-economic disparities in food purchasing patterns among Canadian households: Implications for food policy
Bibliographic record
Abstract
Market-based interventions to improve diets in the population have been the focus of some recent developments in Canadian food policy, but their impact on different population subgroups is not well understood. The goal of this research was to characterize the food purchasing patterns of Canadian households in relation to socio-economic factors through analyses of data from Statistics Canada's Family Food Expenditure Surveys (FOODEX). An analysis of data from the 1996 FOODEX revealed that income and education have independent effects on food selection. Higher income and higher education (particularly a university degree) were both associated with selecting a more nutritious array of foods. Income thresholds, below which food purchasing appeared to be severely constrained, were apparent for several food groups, and an income gradient was observed for vegetables and fruit indicating the impact of income on this food group is similar across the income spectrum. An examination of trends over time (1986-2001), using data from four different waves of FOODEX, revealed income-related disparities in the nutritional quality of food selections that have persisted over time and, in some cases, widened. One notable exception to these trends was the nutrient folate. The positive income gradient in folate (independent of energy) observed in the time periods prior to mandatory folic acid fortification was no longer apparent in 2001, post-fortification. The insights gained from the aforementioned analyses were applied to a current policy issue: the reduction of trans fats in the Canadian food supply. In an analysis of data from the 2001 FOODEX, a higher income was associated with paying a higher price for foods that are major sources of trans fat. These findings raise concerns about the effectiveness of current interventions based on voluntary measures by food manufacturers, given the literature suggesting that such measures are likely to be restricted to higher priced products. This research importantly adds to our limited knowledge of Canadian food consumption patterns, providing insight into the potential impact of some recent changes in food policy on food consumption patterns, and highlighting the need to monitor the impacts of food policy on different population subgroups.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".