Exploring Pathways of Socioeconomic Inequity in Vegetable Expenditure Among Consumers Participating in a Grocery Loyalty Program in Quebec, Canada, 2015–2017
Bibliographic record
Abstract
Vegetable consumption remains consistently low despite supportive policy and investments across the world. Vegetables are available in great variety, ranging in their processing level, availability, cost, and arguably, nutritional value. A retrospective longitudinal study was conducted in Quebec, Canada to explore pathways of socioeconomic inequity in vegetable expenditure. Data was obtained for consumers who participated in a grocery loyalty program from 2015 to 2017 and linked to the 2016 Canadian census. Vegetable expenditure share (%) was examined as a fraction of the overall food basket and segmented by processing level. Panel random effects and tobit models were used overall and to estimate the stratified analysis by median income split. Consumers allocated 8.35% of their total food expenditure to vegetables, which was mostly allocated to non-processed fresh (6.88%). Vegetable expenditure share was the highest in early winter and lowest in late summer. In the stratified analysis, the low-income group exhibited less seasonal variation, allocated less to fresh vegetables, and spent more on canned and frozen compared to the high-income group. Measures of socioeconomic status were all significant drivers of overall vegetable consumption. Consumers with high post-secondary education in the low-income group spent 2% more on vegetables than those with low education. The complexity of observed expenditure patterns points to a need for more specific vegetable consumption guidelines that include provisions by processing level. Implications for education, marketing, intersectional policies, and the role of government are discussed. Governments can scale present efforts and catalyze health-promoting investments across local, state, national, and global food systems.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".