Assessing Health Literacy among Older Adults in Japan A Cross-Sectional Study
Bibliographic record
Abstract
Population aging is occurring faster globally than ever before. By 2030, the population of people aged 60 and over will increase from one billion in 2020 to 1.4 billion, and one in six people in the world will be 60 years old and over according to World Health Organization [WHO]. Facing the pressing challenge, countries are renewing their health and social systems to accommodate this demographic shift. Consequently, health literacy (HL) of older adults becomes an important navigator to maximize healthy lifestyle and access to health care services. However, studies assessing the reality of HL in older adults in a super-aged society are limited, and the factors that have influenced HL over many years remain unclear. This study aims to identify characteristics related to HL by gender and age group and factors influencing HL for older adults in Japan. Using the 47-item Japanese version of the European Health Literacy Survey Questionnaire, a cross-sectional survey was conducted recruiting adults aged 65 and older, living in an urban community in Japan from December 2019 to January 2020. Descriptive, univariate, and multiple regression analyses were used. A total of 367 participants who were members of senior clubs was analyzed in the study. This study found HL was significantly lower in the group aged 75 years and older than in the group aged 65 to 74 years. There was no significant difference in HL between men and women. Based on the results of this study, it is recommended that in order to sustain HL among the older adults in a super-aged society, an environment that promotes HL should be created by capturing characteristics such as skills in using health information media and social skills in the community by gender and age group.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".