Bibliographic record
Abstract
Health literacy as a discrete form of literacy is becoming increasingly important for social, economic and health development. The positive and multiplier effects of education and general literacy on population health, particularly women's health, are well known and researched. However, a closer analysis of the current HIV/AIDS epidemics, especially in Africa, indicates a complex interface between general literacy and health literacy. While general literacy is an important determinant of health, it is not sufficient to address the major health challenges facing developing and developed societies. As a contribution to the health literacy forum in Health Promotion International, this paper reviews concepts and definitions of literacy and health literacy, and raises conceptual, measurement and strategic challenges. It proposes to develop a set of indicators to quantify health literacy using the experience gained in national literacy surveys around the world. A health literacy index could become an important composite measure of the outcome of health promotion and prevention activities, could document the health competence and capabilities of the population of a given country, community or group and relate it to a set of health, social and economic outcomes.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".