Farmers' Market Use Patterns Among Supplemental Nutrition Assistance Program Recipients With High Access to Farmers' Markets
Bibliographic record
Abstract
OBJECTIVE: Evaluate farmers' market (FM) use patterns among Supplemental Nutrition Assistance Program (SNAP) recipients. DESIGN: Cross-sectional survey administered June to August, 2015. SETTING: Cleveland and East Cleveland, OH. PARTICIPANTS: A total of 304 SNAP recipients with children. Participants lived within 1 mile of 1 of 17 FMs. Most were African American (82.6%) and female (88.1%), and had received SNAP for ≥5 years (65.8%). MAIN OUTCOME MEASURES: Patterns of FM shopping, awareness of FM near home and of healthy food incentive program, use of SNAP to buy fruits and vegetables and to buy other foods at FMs, receipt of healthy food incentive program. ANALYSIS: Two-stage cluster analysis to identify segments with similar FM use patterns. Bivariate statistics including chi-square and ANOVA to evaluate main outcomes, with significance at P ≤ .05. RESULTS: A total of 42% reported FM use in the past year. Current FM shoppers (n = 129) were segmented into 4 clusters: single market, public market, multiple market, and high frequency. Clusters differed significantly in awareness of FM near home and the incentive program, use of SNAP to buy fruit and vegetables at FMs, and receipt of incentive. CONCLUSIONS AND IMPLICATIONS: Findings highlight distinct types of FM use and had implications for tailoring outreach to maximize first time and repeat use of FMs among SNAP recipients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".