Identifying At-Risk Subpopulations of Canadians with Limited Health Literacy
Bibliographic record
Abstract
Background. Health literacy, the set of skills for locating, understanding, and using health-related information, is associated with various health outcomes through health behaviors and health care service use. While health literacy has great potential for addressing health disparities stemming from the differing educational attainment in diverse populations, knowledge about subpopulations that share the same risk factors is useful. Objective. This study employed a logistic regression tree algorithm to identify subpopulations at risk of limited health literacy in Canadian adults. Design. The nationally representative data were derived from the International Adult Literacy and Skills Survey (n = 20,059). The logistic regression tree algorithm splits the samples into subgroups and fits logistic regressions. Results. Results showed that the subpopulation comprised of individuals 56 years and older, with household income less than $50,000, no participation in adult education programs, and lack of reading activities (i.e., newspaper, books) was at the greatest risk (82%) of limited health literacy. Other identified subgroups were displayed in an easily interpreted tree diagram. Conclusions. Identified subpopulations organized in tree diagrams according to the risk of limited health literacy inform not only intervention programs targeting unique subpopulations but also future health literacy research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".