Health Literacy of Japanese Elderly who Participated in A Long-term Care Prevention Exercise Program by Household Composition
Bibliographic record
Abstract
Objectives: With the development of services to improve the daily lives of older adults and the use of Internet of Things technology in healthcare in Japan, there is a growing need to address health literacy, especially among older adults living alone. The current study aims to fill this research gap by assessing the current state of health literacy in this population, and potentially providing guidance for future support strategies.Methods: In this study, 22 older female participants in long-term care preventive services were assessed for health literacy, cognitive function, and activities of daily living using the Household Composition and Basic Demographic Information, the Health Literacy Scale, the Japanese version of the Montreal Cognitive Assessment, and the Occupational Self-Assessment-Short Form.Results: The household composition was 13 older adults living alone and nine living with others. Older adults living alone had significantly lower functional health literacy than those living with others, which affected their ability to understand and apply health information. Difficulties reading health materials were also prevalent in this group, suggesting that visual impairment or a lack of assistive devices may affect their understanding of health information. Considering these specific needs through tailored strategies is essential for helping older adults to adapt to an increasingly digital society.Conclusions: Understanding the current state of functional health literacy among older adults living alone in the community is essential for developing strategies to improve their well-being, prevent isolation, and enable them to make informed decisions about their health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".