Low-income adults’ perceptions of farmers’ markets and community-supported agriculture programmes
Bibliographic record
Abstract
OBJECTIVE: To better understand low-income adults' attitudes towards participating in farmers' markets, community-supported agriculture (CSA) and nutrition education programming. DESIGN: Focus groups were held with a diverse sample of adults. Interviews were transcribed verbatim and analysed using thematic analysis. SETTING: Three affordable housing communities in Washington, DC, USA. SUBJECTS: Participants included twenty-eight residents of the three affordable housing communities. RESULTS: Four major themes emerged across groups, along with several sub-themes within each theme. These included: (i) perceptions of farmers' markets (benefits, barriers, current participation and knowledge); (ii) perceptions of CSA (benefits, barriers and questions/concerns); (iii) need/interest in additional programming (nutrition education, non-nutrition education, qualities of programming and perceived barriers); and (iv) current health knowledge and behaviours (dietary behaviours, health recommendations and health concerns). CONCLUSION: Adults living in urban, affordable housing communities desire access to healthy foods, but are limited by cost. Programmes could have a higher likelihood of success if they accept benefits like SNAP (the Supplemental Nutrition Assistance Program), are heavily marketed and incorporate culturally relevant nutrition education components.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".