Knowledge, attitude, and practice of using food labels among medical students: A cross-sectional study in Haiphong, Vietnam
Bibliographic record
Abstract
Food labeling is an essential tool that provides consumers with dietary guidelines. The increased use of food labels can improve people’s health and prevent nutrition-related problems. In Vietnam, more evidence is needed regarding the practice of reading of food labels, particularly among medical students. A cross-sectional study was conducted using a self-administered structured questionnaire from January 25 to February 30, 2022, on 1,120 medical students at Haiphong University of Medicine and Pharmacy in Vietnam. The study revealed that only about 20% of respondents understood the information on ingredients, nutritional facts, and allergens. 99.1% of respondents believed that food labels are helpful for consumers. 80% of respondents considered reading food labels necessary or very necessary. Only 2.9% strongly believed, and 33.8% believed in food labels. When purchasing foods, the percentage of respondents who often and always read food labels was 23% and 7.5%, respectively. Nearly 80% of respondents often or always prioritize buying food with labels. In addition, the price was the most critical factor in product choice for 78.7% of respondents. Multivariate logistic regression analysis showed that type of residence and nutrition knowledge were associated with reading food labels. Few Vietnamese medical students read food labels despite considering it necessary. Medical training programs should emphasize the importance of reading food labels for future doctors to improve the population’s health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".