Up to a quarter of the Australian population may have suboptimal health literacy depending upon the measurement tool: results from a population-based survey
Bibliographic record
Abstract
The objective of this paper is to measure health literacy in a representative sample of the Australian general population using three health literacy tools; to consider the congruency of results; and to determine whether these assessments were associated with socio-demographic characteristics. Face-to-face interviews were conducted in a stratified random sample of the adult Victorian population identified from the 2004 Australian Government Electoral Roll. Participants were invited to participate by mail and follow-up telephone call. Health literacy was measured using the Rapid Estimate of Adult Literacy in Medicine (REALM), Test of Functional Health Literacy in Adults (TOFHLA) and Newest Vital Sign (NVS). Of 1680 people invited to participate, 89 (5.3%) were ineligible, 750 (44.6%) were not contactable by phone, 531 (32%) refused and 310 (response rate 310/1591, 19.5%) agreed to participate. Compared with the general population, participants were slightly older, better educated and had a higher annual income. The proportion of participants with less than adequate health literacy levels varied: 26.0% (80/308) for the NVS, 10.6% (51 33/310) for the REALM and 6.8% (21/309) for the TOFHLA. A varying but significant proportion of the general population was found to have limited health literacy. The health literacy measures we used, while moderately correlated, appear to measure different but related constructs and use different cut offs to indicate poor health literacy.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".