An International Comparison of the Association Among Literacy, Education, and Health Across the United States, Canada, Switzerland, Italy, Norway, and Bermuda: Implications for Health Disparities
Bibliographic record
Abstract
The relationship between education and health is well-established, but theoretical pathways are not fully understood. Economic resources, stress, and health behaviors partially explain how education influences health, but further study is needed. Previous studies show that health literacy mediates the education-health relationship, as do general literacy skills. However, little is known whether such mediation effects are consistent across different societies. This study analyzed data from the International Assessment of Adult Literacy and Life Skills Survey conducted in Canada, the United States, Italy, Norway, Switzerland, and Bermuda to investigate the mediation effects of literacy on the education-health relationship and the degree of such mediation in different cultural contexts. Results showed that literacy skills mediated the effect of education on health in all study locations, but the degree of mediation varied. This mediation effect was particularly strong in Bermuda. This study also found that different types of literacy skills are more or less important in each study location. For example, numeracy skills in the United States and prose (reading) literacy skills in Italy were stronger predictors of health than were other literacy skills. These findings suggest a new direction for addressing health disparities: focusing on relevant types of literacy skills.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".