A Canadian exploratory study to define a measure of health literacy
Bibliographic record
Abstract
This study undertook a qualitative exploration of an operational definition of health literacy and an examination of quantitative measures of health literacy skills. We interviewed 229 older Canadian adults. First we engaged them in open-ended discussions about their search for information on a self-selected health topic. Next we administered nine self-report items on health literacy skills, and then task-performance items. Task-performance questions were based on two published reading passages on five levels of difficulty to measure 'understanding' of health-related material. The Rapid Estimate of Adult Literacy in Medicine (REALM) was also administered as the comparison for criterion-related validity. Our open-ended questions elicited responses about the processes that people undergo when they attempt to access, understand, appraise and communicate health information. Qualitative findings revealed complexities in participants' interpretation of the meaning of all four health literacy skills. These descriptive findings add new knowledge about health literacy as a construct. Participants agreed with most of the self-report statements, thus indicating high belief in their own health literacy. REALM scores ranged from 45 to 66 with an average of 65 and standard deviation of 2.5. Quantitative scores on the reading passages were modestly correlated with scores on the REALM. The sum scale of self-report items, however, did not correlate with task-performance items, suggesting that the different types of items may not be measuring the same construct. We suggest that self-report items need more development and validation. Our study makes a contribution in exploring the complexities of measuring health literacy skills for general health contexts.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".