Theoretical framework of concerns about falling in older people: the role of health literacy
Bibliographic record
Abstract
Adherence and participation can be improved in health programs for older people with concerns about falling. While health literacy empowers older people to have greater control over their health, little is known about the extent to which health literacy influences health behaviours associated with concerns about falling in older people. This study aimed to synthesise current findings on health literacy, concerns about falling and falls to propose a multicomponent theoretical model on health literacy and concerns about falling. The model was developed based on a review of the literature, existing frameworks and models on health literacy and concerns about falling. Existing evidence on the relationship between health literacy and concerns about falling in older people is limited. Evidence from other research areas, however, shows that health literacy is closely related to many of the determinants of concerns about falling. More research is needed to clarify the impact of health literacy on intervention adherence and decision-making processes of older people with concerns about falling. Our model offers a novel perspective on the role of health literacy in health behaviours associated with concerns about falling, suggesting new research directions and providing insights for clinicians to consider health literacy when managing older patients with concerns about falling.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".