Tracing the Single and Combined Contributions of Home-Grown Supply and Health Literacy on Fruit and Vegetable Consumption: An Empirical Exploration in Rural India
Bibliographic record
Abstract
Low fruit and vegetable consumption (FVC) remains a global health challenge. Fostering subsistence agriculture through the production and home-grown consumption (HGC) of fruits and vegetables are seen as potential strategies for improving overall FVC, in particular, for developing countries like India. In addition, educational strategies targeting FVC health literacy are also used. Little evidence has documented a connection between these two strategies. We examine the single and combined influence of HGC and health literacy with regard to benefits from fruits and vegetable consumption. Data were collected from 427 rural households in the state of Odisha, India. Three outcomes were examined: FVC, as well as fruit and vegetables separately. Linear and Poisson regression were used to examine the association among home-grown consumption (HGC), FVC health literacy, and the FVC outcomes. Findings show that HGC, but not FVC health literacy, was directly associated with FVC (β = 0.65, SE = 0.10, p = 0.008) and vegetable consumption (β = 0.57, SE = 0.11, p = 0.02). However, both HGC (β = 0.58, SE = 0.05, p < 0.01) and FVC health literacy (β = −0.07, SE = 0.02, p = 0.001) were associated with fruit consumption. In addition, HGC effect is concentrated among participants who reported low FVC health literacy, especially on overall FVC and vegetables alone. Results are discussed in relation to the beneficial role played by HGC in those particularly vulnerable households who perceived little FVC health literacy. Our results provide insights on novel improved FVC consumption across all population segments. Future research should explore the complex interplay between agricultural policies and educational programs in the design of interventions promoting fruit and vegetable production and consumption.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".