Exploring the impacts of a farmers’ market healthy food subsidy in Canada on the diet quality, household food insecurity, and psychosocial well-being of adults with low incomes
Bibliographic record
Abstract
Dietary inequities and their contribution to health inequities are a significant public health concern in Canada. The growing prevalence of household food insecurity in Canada and its associated negative dietary, psychosocial, and overall health consequences highlight the need for policies that improve the financial circumstances of low-income households. Farmers’ market healthy food subsidy programs demonstrate potential to reduce financial barriers to purchasing and consuming nutritious foods. In Canada, the British Columbia Farmers’ Market Nutrition Coupon Program (FMNCP) provides coupons to low-income households to purchase healthy foods at farmers’ markets. However, its effects have not been rigorously investigated, nor is it known whether program effects vary across participant subgroups. Accordingly, this dissertation had two objectives: 1) to conduct a parallel-group pragmatic randomized controlled trial (RCT) to examine the impact of the FMNCP on the diet quality (primary outcome), household food insecurity, mental well-being, sense of community, risk of malnutrition (secondary outcomes), and subjective social status (exploratory outcome) of adults with low incomes post-intervention and 16 weeks post-intervention, and 2) to explore whether program impacts on diet quality varied across participant subgroups using a causal forest analysis. Adults with low incomes were randomized to a FMNCP intervention (n=143) or a no-intervention control group (n=142). The FMNCP group received 16 coupon sheets valued at $21/sheet over 10–15 weeks to purchase healthy foods at farmers’ markets. Participants completed a questionnaire and two 24-hour dietary recalls at baseline (0 weeks), post-intervention (10–15 weeks), and 16 weeks post-intervention (26–31 weeks). The RCT found that the FMNCP reduced short-term household food insecurity by 79% but had no effect on diet quality, mental well-being, sense of community, malnutrition risk, or subjective social status. The causal forest did not detect heterogeneous treatment effects on diet quality. These findings indicate that the FMNCP reduces short-term household food insecurity. However, to effectively improve diet quality and other outcomes, larger and longer-term subsidies may be needed. Moreover, the program should be implemented alongside additional multi-level, multi-sectoral approaches that address the many factors influencing the diet quality and psychosocial well-being of adults with low incomes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".