Association between fruit and vegetable consumption and chronic diseases among food pantry users
Bibliographic record
Abstract
Abstract Introduction Fruit and vegetable (FV) consumption can be a protective factor for chronic diseases, but few studies have investigated FV’s impact on health in the context of food/nutrition assistance system. Methods We used three health survey data collected in Upstate New York communities to construct a predictive model of food pantry use. The model was applied to a Northeastern US regional subset of SMART Behavioral Risk Factor Surveillance System (BRFSS) data to identify potential food pantry users. The associations between FV intake and diabetes, hypertension, and BMI were examined through multivariable logistic regression and linear regression analyses with food pantry use as a potential effect modifier. Results The analysis dataset had 5,257 respondents, and 634 individuals were estimated as food pantry users. Consumption of vegetables was associated with decreased odds of hypertension and a lower BMI regardless of food pantry use. Consumption of fruits was associated with decreased odds of diabetes regardless of food pantry use. The association between fruit consumption and BMI was modified by food pantry use. Among food pantry users, consumption of fruits was associated with a greater BMI, while among food pantry non-users, it was associated with a lower BMI. Conclusion The overall protective effects of increased FV consumption on chronic diseases suggest that increasing FV availability in food pantries may not only alleviate hunger but also improve health. Further research is needed to investigate the role of fruit including 100% fruit juice consumption and BMI among food pantry users.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".