Always Open, Seven-Eleven: Education Targeting Healthier Food Choices in a High Convenience Store Density Area in Taipei
Bibliographic record
Abstract
To enhance children's health, the promotion of nutrition literacy in school is vital as it helps prevent the development of health conditions and diseases and maintain healthy lifestyles. Taiwan features the top highest ratio of convenience stores per population density. Convenience stores, an increasingly popular dining place, were linked to the development of eating behavior and body weight issues in children. An eight-week classroom-based nutrition intervention, employing the Traffic Light Diet as a framework, targeting children's perception of and intention to visit the convenience store was implemented. The study conducted a quasi-experimental pretest-posttest research design with a comparison group. A total of 49 students participated in the study, with 25 in the intervention and 24 in the comparison group. Data were collected by utilizing surveys, interviews, and observations. The study's findings demonstrated the positive trajectory of the impact of this intervention on increasing food-and-nutrition-related knowledge and improving healthier diet choices at convenience stores among children. One main theme was identified in coding interviews: parent involvement in meal preparation may reduce convenience store use and increase consumption of vegetables among children. Assessing the influence of parental support for healthy dietary choices, eating nutritious foods at home, and involving the family in meal preparation is an area for future research.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".