Health literacy and chronic disease management: drawing from expert knowledge to set an agenda
Bibliographic record
Abstract
Understanding the nature and impact of health literacy is a priority in health promotion and chronic disease prevention and treatment. Health literacy comprises the application of a broad set of skills to access, comprehend, evaluate, communicate and act on health information for improved health and well-being. A complex concept, it involves multiple participants and is enacted across a wide variety of contexts. Health literacy's complexity has given rise to challenges achieving a standard definition and developing means to measure all its dimensions. In May 2013, a group of health literacy experts, clinicians and policymakers convened at an Expert Roundtable to review the current state of health literacy research and practice, and make recommendations about refining its definition, expanding its measurement and integrating best practices into chronic disease management. The four-day knowledge exchange concluded that the successful integration of health literacy into policy and practice depends on the development of a more substantial evidence base. A review of the successes and gaps in health literacy research, education and interventions culminated in the identification of key priorities to further the health literacy agenda. The workshop was funded by the UBC Peter Wall Institute for Advanced Studies, Vancouver.
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 | 0.134 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.005 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.026 | 0.045 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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".