Socioeconomic and sociodemographic determinants associated with fruit and vegetable consumption among mothers and homes of schoolchildren in Jalisco
Bibliographic record
Abstract
INTRODUCTION: Introduction: increasing fruit and vegetable consumption is a priority. It has been prioritized as a fundamental objective of public policies worldwide. Given that such consumption in schoolchildren in Jalisco (Mexico) is below the dietary recommendations it is crucial to identify the determinants that influence this consumption to promote the development of contextualized actions that improve it. Objective: to identify the socioeconomic and sociodemographic determinants of fruit and vegetable consumption among mothers and households of schoolchildren in Jalisco, Mexico. Method: an analytical, cross-sectional study carried out during the first quarter of 2020. A validated food consumption frequency and a questionnaire on sociodemographic and socioeconomic factors were used for its development. Results: a lower educational level of the mothers of schoolchildren was associated with a lower consumption of vegetables by schoolchildren. In turn, a lower household income level was associated with a lower consumption of fruits in schoolchildren. Conclusions: a low educational level of mothers and a low household income were determinants associated with fruit and vegetable consumption in schoolchildren. However, there were differences in the determinants for fruits and vegetables. It is essential to consider these factors and their differences in order to plan actions that contribute to improving fruit and vegetable intake in the school 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".