“It's Changed Our Way of Eating a Lot”: Experiences of Nutrition and Health Improvements After Participation in an Urban Home Garden Program
Bibliographic record
Abstract
To elucidate the perceived health benefits of an urban home gardening and nutritional education program in a population at high cardiometabolic risk.Qualitative data collected via in-depth, semistructured interviews in Spanish or English.Community-based program offering supported urban home gardening together with nutrition education in Santa Clara County, CA.A total of 32 purposively sampled low-income participants in an urban home gardening program. Participants were primarily female (n = 24) and Latino/a (n = 22).Perceptions of the nutrition and health benefits of education-enhanced urban home gardening.Bilingual researchers coded transcripts using a hybrid inductive and deductive approach. Two coders double coded at intervals, independently reviewed coding reports, organized content into key themes, and selected exemplary quotations.The most salient perceived impacts were greater food access, increased consumption of fresh produce, a shift toward home cooking, and decreased fast food consumption. Participants attributed these changes to greater affordability, freshness, flavor, and convenience of their garden produce; increased health motivation owing to pride in their gardens; and improved nutritional knowledge. Participants also reported improved physical activity, mental health, and stress management; some reported improved weight and adherence to diabetes-healthy diets.Education-enhanced urban home gardening may facilitate multidimensional nutrition and health improvements in marginalized populations at high cardiometabolic risk.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".