A Photovoice Inquiry Into the Impacts of a Subsidized CSA Program on Participants’ Health
Bibliographic record
Abstract
Although consumption of fruits and vegetables is associated with reduced risk of disease, many Americans do not eat the recommended quantity or variety. This is especially true for low-income populations, who may face significant barriers to accessing fresh produce, including cost. Community-supported agriculture (CSA) Partnerships for Health is a subsidized community-supported agriculture program designed to reduce barriers to accessing fresh produce in a low-income population. This Photovoice study gave participants ( n = 28) an opportunity to take photos representing how the program affects their lives. The aim was to understand the program’s impact from the perspective of CSA members. Participants had 2 to 4 weeks to take photographs, and then selected a few to discuss during a subsequent focus group. Through this discussion, we learned that participants see the program as (a) supporting positive changes to their physical and social health and (b) facilitating learning about new foods, cooking, and agriculture. The study suggests that a reduced-cost CSA membership that incorporates cooking education supports participants’ ability to try new foods, build skills, and improve health outcomes.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".