Health Literacy: What Is It and Why Is It Important to Measure?
Bibliographic record
Abstract
This report summarizes the proceedings of the first Outcome Measures in Rheumatology Clinical Trials (OMERACT) Health Literacy Special Interest Group workshop at the OMERACT 10 conference. Health literacy refers to an individual's capacity to seek, understand, and use health information. Discussion centered on the relevance of health literacy to the rheumatology field; whether measures of health literacy were important in the context of clinical trials and routine care; and, if so, whether disease-specific measures were required. A nominal group process involving 27 workshop participants, comprising a patient group (n = 12) and a healthcare professional and researcher group (n = 15), confirmed that health literacy encompasses a broad range of concepts and skills that existing scales do not measure. It identified the importance and relevance of patient abilities and characteristics, but also health professional factors and broader contextual factors. Sixteen themes were identified: access to information; cognitive capacity; disease; expression/communication; finances; health professionals; health system; information; literacy/numeracy; management skills; medication; patient approach; dealing with problems; psychological characteristics; social supports; and time. Each of these was divided further into subthemes of one or more of the following: knowledge, attitude, attribute, relationship, skill, action, or context. There were virtually no musculoskeletal-specific statements, suggesting that a generic health literacy tool in rheumatology is justified. The detailed concepts across themes provided new and systematic insight into what needs to be done to improve health literacy and consequently reduce health inequalities. These data will be used to derive a more comprehensive measure of health literacy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".