Mapping Health Literacy Strategies and Outcomes in Older Adults: A Scoping Review
Bibliographic record
Abstract
Background: Older adults are currently the fastest aging population in Canada and the United States, yet, have the lowest levels of health literacy in the population. Research supports that consequences of low health literacy are poorer related health outcomes and health status. The aim of this scoping review was to summarize the extent of the literature of health literacy on health outcomes in older adults and identify strategies that aim to increase health literacy. Methods: A scoping review was completed via online search of the literature using Medline, ERIC and PsychInfo databases, using key terms: functional health literacy and older adults. Inclusion criteria included the following: explicit focus on health literacy, publication in English, older adult population, primary research studies conducted in Canada or USA. Results: The initial search yielded 238 articles, after title screening and abstract reviewing 48 articles were screened for the full text. 15 articles met the inclusion criteria and were included. This scoping review identified several interventions in heart failure, asthma, hypertension and diabetes, that aim to increase health literacy in the elderly population, however no studies found change in health literacy and health outcomes in the long term. Conclusion: There is much future work needed to address health literacy in older adults on interventions, long term health outcomes and education for health care providers.
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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 | 0.024 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".