Which literacy for health promotion: health, food, nutrition or media?
Bibliographic record
Abstract
Education and literacy are important aspects of health promotion. The potential for health literacy to promote healthier choices has been widely examined, with studies variously incorporating food literacy, nutrition literacy and/or media literacy as components of health literacy, rather than treating each as unique concepts for health promotion. This study examines similarities and differences across health literacy, food literacy, nutrition literacy and health-promoting media literacy to highlight how each literacy type theorizes the relationship between education and health. A meta-review of existing scoping and systematic reviews examining literacy conceptualizations was conducted to examine the four literacies. Representative concept definitions were extracted and key competencies (or skills) and desired consequences were identified and grouped into subcategories for analysis. This study located 378 articles, of which 17 scoping/systematic reviews were included (10 for health literacy, 3 for food, 1 for nutrition and 3 for media). Representative concept definitions of the four literacy types revealed three skill categories (information acquisition, information analysis, and the application of information) and three categories of desired consequences (knowledge, attitudes and behaviors), with each of the four literacy types emphasizing varied collections of skills and desired consequences. Despite perceived similarities in content, health, food, nutrition and media literacy conceptualize the relationship between education and health differently, emphasizing the distinct types of knowledge to promote health-related outcomes. A better understanding of the differences between these four literacies will lead to informed decision making for researchers, educators and health practitioners in intervention design and implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".