Experiences and Perceived Outcomes of Low-Income Adults During and After Participating in the British Columbia Farmers’ Market Nutrition Coupon Program: A Longitudinal Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The British Columbia Farmers' Market Nutrition Coupon Program (FMNCP) is a farmers' market food subsidy program that provides low-income households with coupons valued at $21/wk for 16 weeks to purchase healthy foods at participating BC Association of Farmers' Markets members' markets. OBJECTIVE: This study aimed to explore changes, differences, and similarities in participants' experiences and perceived short-term outcomes during and after participating in the FMNCP. DESIGN: A longitudinal qualitative research approach was used to conduct a recurrent cross-sectional analysis. Data generation and analysis were guided by Freedman et al's theoretical framework of nutritious food access. Data generation occurred during 2019 FMNCP program (time 1) and 4 to 7 weeks after (time 2) the program year ended. Data at each time point were analyzed separately using directed content analysis, followed by a comparative analysis to identify changes, differences, and similarities between time points. PARTICIPANTS: Twenty-eight adult participants were interviewed during the program; 24 were re-interviewed post program. SETTING: Three communities in British Columbia, Canada. RESULTS: Three themes were generated: temporary relief and engagement; lasting experiences and outcomes; enhancing participant experiences and outcomes. The first theme related to how participants' experiences and perceived outcomes, such as increased financial support and improved diet quality and health, were temporary. The second theme reflected positive lasting experiences and outcomes from participating in the FMNCP, including increased food and nutrition knowledge and enhanced social ties. The third theme focused on enhancing participants' program experiences and outcomes, including increasing the duration of food subsidies. CONCLUSION: The FMNCP temporarily enhanced access to nutritious foods and had lasting positive effects on participants' nutrition-related knowledge and social outcomes. Nevertheless, participants struggled to maintain healthy eating practices post program due to financial constraints. Expanding farmers' market subsidy programs may improve access to nutritious foods; maintain positive dietary, social, and health outcomes for participants; and reach more low-income households.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".