Measuring Health Literacy in Individuals With Diabetes
Bibliographic record
Abstract
OBJECTIVE: To identify instruments used to measure health literacy and numeracy in people with diabetes; evaluate their use, measurement scope, and properties; discuss their strengths and weaknesses; and propose the most useful, reliable, and applicable measure for use in research and practice settings. METHODS: A systematic literature review was conducted to identify the instruments. Nutbeam's domains of health literacy and a diabetes health literacy skill set were used to evaluate the measurement scope of the identified instruments and to evaluate their applicability in people with diabetes. RESULTS: Fifty-six studies were included, from which one diabetes-specific (LAD) and eight generic measures of health literacy (REALM, REALM-R, TOFHLA, s-TOFHLA, NVS, 3-brief SQ, 3-level HL Scale, SILS) and one diabetes-specific (DNT) and two generic measures of numeracy (SNS, WRAT) were identified. These instruments were categorized into direct measures, that is, instruments that assess the performance of individuals on health literacy skills and indirect measures that rely on self-report of these skills. The most commonly used instruments measure selective domains of health literacy, focus mainly on reading and writing skills, and do not address other important skills such as verbal communication, health care system navigation, health-related decision making, and numeracy. The structure, mode, and length of administration and measurement properties were found to affect the applicability of these instruments in clinical and research settings. Indirect self- or clinician-administered measures are the most useful in both clinical and research settings. CONCLUSION: This review provides an evaluation of available health literacy measures and guidance to practitioners and researchers for selecting the appropriate measures for use in clinical settings and research applications.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".