Healthy eating for life: rationale and development of an English as a second language (ESL) curriculum for promoting healthy nutrition
Bibliographic record
Abstract
Low health literacy contributes significantly to cancer health disparities disadvantaging minorities and the medically underserved. Immigrants to the United States constitute a particularly vulnerable subgroup of the medically underserved, and because many are non-native English speakers, they are pre-disposed to encounter language and literacy barriers across the cancer continuum. Healthy Eating for Life (HE4L) is an English as a second language (ESL) curriculum designed to teach English language and health literacy while promoting fruit and vegetable consumption for cancer prevention. This article describes the rationale, design, and content of HE4L. HE4L is a content-based adult ESL curriculum grounded in the health action process approach to behavior change. The curriculum package includes a soap opera-like storyline, an interactive student workbook, a teacher's manual, and audio files. HE4L is the first teacher-administered, multimedia nutrition-education curriculum designed to reduce cancer risk among beginning-level ESL students. HE4L is unique because it combines adult ESL principles, health education content, and behavioral theory. HE4L provides a case study of how evidence-based, health promotion practices can be implemented into real-life settings and serves as a timely, useful, and accessible nutrition-education resource for health educators.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".