Factors associated with consumption of fruits and vegetables among Community Kitchens customers in Lima, Peru
Bibliographic record
Abstract
Community Kitchens (CKs) are one of the main food providers to low-income families in Peru and may encourage healthier diets. We aimed to determine the prevalence of fruit and vegetable consumption and associated sociodemographic and behavioral factors among CKs customers. A cross-sectional study enrolling customers of 48 CKs in two areas of Lima, Peru, was performed. The self-reported amount of fruits and vegetables consumed (< 5 vs. ≥ 5 servings/day) was the outcome. The exposures were grouped in sociodemographic variables (i.e. age, gender, education level, etc.), and self-reported intention to change eating- and exercise-related habits in the last four weeks just prior to the interview. Prevalence ratios (PR) were estimated using Poisson regression. Data from 422 subjects were analyzed, 328 females (77.9%), mean age 43.7 (± 14.5) years. Only 36 (8.5%; 95% CI 5.9%-11.2%) customers reported consuming ≥ 5 servings of fruits and vegetables daily. This pattern was 4-fold more likely among those with higher levels of education (≥ 12 vs. < 7 years), and 64% less common for migrants relative to non-migrants. In terms of intentions to change habits, those who reported having tried to reduce sugar consumption or to eat more fruits were up to 90% more likely to meet the ≥ 5 servings/day target. A substantial gap in the consumption of ≥ 5 servings of fruits and vegetables/day was found among CKs customers that does not appear to be dependent on familial income. The profiles reported in this study can inform appropriate strategies to increase healthier eating in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".