Canadian Men’s Health Literacy: A Nationally Representative Study
Bibliographic record
Abstract
Though men's health promotion has attracted increased research attention, conspicuously absent have been empirical insights to health literacy levels within and across male subgroups. Recent advancements in the measurement of health literacy have made available avenues for evaluating individual and social determinants of health literacy. Important insights can be drawn to detail patterns and diversity among men as a means to informing the design, implementation, and evaluation of tailored health promotion programs. Drawing on 2000 Canada-based men's responses to the Health Literacy Questionnaire, correlations between demographic variables and six health literacy scales are described. Low income, low education, and living alone were associated with men's low health literacy, with the strongest effect sizes for the "Social support for health" and "Actively engaged with health care professionals" scales. Multiple linear regressions confirmed low income as the strongest predictor of men's low health literacy in all the scales except "Appraisal of health information." Low income, self-identifying as gay, bisexual, or other, and living alone were strongly predictive of low scores on the "Social support for health" scale. The findings affirm the importance of considering men's health literacy and inequities to advance effective men's health promotion programs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".