Literacy and health research in Canada: Where have we been and where should we go? Canadian
Bibliographic record
Abstract
This article reviews current literature and research on literacy and health and identifies priorities for research on this topic in Canada. Information sources included documents found through an environmental scan, the Alpha Plus collection and a computer search of recent documents. The information was analyzed using a conceptual framework. The review found that low literacy has direct and indirect impacts on health. Families are at risk due to difficulty reading medication prescriptions, baby formula instructions and health and safety education materials. People with lower levels of literacy tend to live and work in less healthy environments. They have more difficulties obtaining employment and income security. Determinants of literacy include: education, early childhood development, aging, living and working conditions, personal capacity/genetics, gender and culture. Action is needed to improve literacy and health through a combination of health communication, education and training, community development, organizational development, and policy development. There is some evidence that such interventions can have a positive effect on health, particularly when combined with one another. Further program and policy development requires greater evidence and evaluation of existing initiatives, more cost/benefit analyses, more culturally specific studies, and greater attention to current social trends and needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".