Exploring experiences and perceived program outcomes of low-income adults participating in the British Columbia Farmers’ Market Nutrition Coupon Program
Bibliographic record
Abstract
Introduction: The British Columbia Farmers’ Market Nutrition Coupon Program (BC FMNCP) is a food subsidy program that provides low-income households coupons valued at $21/week for 16 weeks to purchase healthy foods in BC farmers’ markets, along with nutrition skill-building activities. Objectives: Two studies were conducted. The overarching purpose of these studies was to explore experiences and perceived program outcomes of low-income adults participating in the FMNCP, and provide suggestions to enhance participants’ experiences and outcomes of farmers’ market food subsidy programs. Methods: Both studies used qualitative description methodology. Semi-structured interviews were conducted with FMNCP participants during the 2019 farmers’ market season and post-program. These data were first analyzed cross-sectionally using directed content analysis. The initial coding scheme was guided by Freedman et al.’s theoretical framework, followed by inductive coding of data that did not fit within the framework. Data were then analyzed longitudinally to generate themes that described changes, differences, and similarities in participants’ experiences and perceived outcomes during and after the FMNCP. Results: Three themes emerged from the first cross-sectional analysis. The first theme was related to how the FMNCP promoted a sense of autonomy and dignity for participants. The second theme was related to how the FMNCP increased social connections and fostered a sense of community for participants. The third theme highlighted constraints experienced by participants, such as limited food variety in rural farmers’ markets and challenges with redeeming coupons. Three themes emerged from the longitudinal analysis. The first theme related to how participants’ experiences and perceived outcomes were temporary and changed after the FMNCP. The second theme outlined lasting experiences and outcomes that resulted from participating in the FMNCP. The third theme focused on participants’ suggestions to improve the FMNCP to better meet their needs. Conclusions: Participation in the FMNCP facilitated access to nutritious foods and enhanced participants’ diet quality and health. Yet, many experiences and perceived outcomes were temporary due to the time-limited nature of the program. Findings may help improve or expand farmers’ market food subsidy programs to better meet the needs of 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".