Bibliographic record
Abstract
Health literacy is becoming an important issue for nurses, pharmacists, health educators, and other health professionals. Studies are currently suggesting that low health literacy affects health care budgets, health outcomes, and adherence to medication regimens, not to mention an individual's ability to control or prevent illness and disease. At the same time, the amount of health information available to consumers increases every day, most of it written for a highly literate audience. National and international initiatives are developing to address the issue of health literacy, but few consider the library an obvious partner for these important projects. This paper overviews the key issues surrounding health literacy, outlining several initiatives and the methodologies used to evaluate levels of health literacy. Strategies for developing easy-to-read health materials will be explored. Finally, possible roles for libraries and librarians in health literacy will be examined, along with suggestions for further reading.
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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.030 | 0.016 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".