Nutritional label use and understanding among Mexican older persons: a secondary study of National Health and Nutrition Survey (ENSANUT 2021)
Bibliographic record
Abstract
BACKGROUND: Front package warning labeling (FWL) was implemented in Mexico in 2020 as part of a strategy to raise food-related knowledge. However, limited media coverage, a lack of awareness among health professionals, and the usage of technical terminology appear to be impediments affecting many groups of the population, particularly older persons. OBJECTIVE: Analyze the profile of nutritional label use and understanding among older persons taking into account different factors that might have an effect on it. MATERIAL AND METHOD: The use and knowledge of FWL were assessed using a representative sample of 1884 older individuals from the 2021 National Health and Nutrition Survey (ENSANUT). Logistic regression was used to determine the association of knowing or using FWL, adjusted by age, sex, able to read, educational level, living with diabetes, hypertension, and social security status. RESULTS: The probability of reading and using the FWL was influenced by lower age, being female, higher education level and having social security. There was no discernible effect of living with diabetes or hypertension. CONCLUSIONS: Nutritional education tailored to the circumstances of older persons in Mexico is timely.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".