Differences in Health Literacy of Older Adults According to Sociodemographic Characteristics
Bibliographic record
Abstract
BACKGROUND: The research is based on the concept of health literacy. This is the “sixth vital sign” to navigating the healthcare system and raising self-efficacy in the field of healthcare. AIM: The study aim was to present the health literacy of the elderly in the Savinja statistical region in Slovenia. METHODS: The study was based on a non-experimental quantitative research approach. The study included 199 elderly people aged 65 and more, without the presence of dementia. The data were collected using the Health Literacy and Montreal Cognitive Assessment questionnaire. We used a non-random, convenience sampling. RESULTS: We established that in the study population, 64.8 % of the population was below the level of acceptable general health literacy, whereas only 3.5 % of this population had an excellent level of health literacy. Statistically significant differences in the level of health literacy are shown in the field of healthcare treatment in older adults living with their families (F = 5,198; p ˂ 0,001). Respondents who engaged in activities in day care centres also had a higher level of health literacy (t = 3,738; p < 0,001). People with low health literacy, who use health services more frequently, should be given access to health education, based on individual presentation of contents, supported by andragogical knowledge. CONCLUSION: The health literacy of older adults is the basis for their greater care for their own health and a better quality of life.
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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 | 0.003 |
| 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".