The effect of a fresh produce incentive paired with cooking and nutrition education on healthy eating in low-income households: a pilot study
Bibliographic record
Abstract
OBJECTIVE: This study pilot-tested combining financial incentives to purchase fruits and vegetables with nutrition education focused on cooking to increase the consumption of fruits and vegetables and improve attitudes around healthy eating on a budget among low-income adults. The goal of the pilot study was to examine implementation feasibility and fidelity, acceptability of the intervention components by participants and effectiveness. DESIGN: The study design was a pre-post individual-level comparison without a control group. The pilot intervention included two components, a scan card providing free produce up to a weekly maximum dollar amount for use over a 2-month period, and two sessions of tailored nutrition and cooking education. Outcomes included self-reported attitudes about healthy eating and daily fruit and vegetable consumption from one 24-h dietary recall collected before and after the intervention. SETTING: Greater Minneapolis/St. Paul area in Minnesota. PARTICIPANTS: Adults (n 120) were recruited from five community food pantries. RESULTS: Findings indicated that the financial incentive component of the intervention was highly feasible and acceptable to participants, but attendance at the nutrition education sessions was moderate. Participants had a statistically significant increase in the consumption of fruit, from an average of 1·00 cup/d to 1·78 cups/d (P < 0·001), but no significant change in vegetable consumption or attitudes with respect to their ability to put together a healthy meal. CONCLUSIONS: While combining financial incentives with nutrition education appears to be acceptable to low-income adult participants, barriers to attend nutrition education sessions need to be addressed in future research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".