Successful Aging Across Middle Versus High-Income Countries: An Analysis of the Role of eHealth Literacy Associated With Loneliness and Well-Being
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: "Successful aging" concerns the process of growing older while maintaining physical, cognitive, and social well-being, emphasizing independence for overall satisfaction and quality of life. We investigate the impact of eHealth literacy on reducing loneliness and sustaining well-being during the pandemic, comparing middle- and high-income countries. RESEARCH DESIGN AND METHODS: Online surveys were conducted between April 4, 2020, and September 30, 2021, collecting responses (N = 2,091) from medium- and high-income countries in Europe, Asia, and North America. T-tests and ANOVAs were used to test how sociodemographic predictors were associated with differences in e-Health literacy, loneliness, and well-being. RESULTS: Respondents from high-income countries reported significantly higher well-being scores than those from middle-income countries and respondents from high-income countries had significantly higher e-HEALS (e-Health literacy) scores compared to middle-income countries. No significant difference was observed in loneliness scores between high-income and middle-income country respondents. Well-being is associated with age, with younger adults (18-29 years) and those aged 40+ reporting higher levels. Higher education and income are linked to greater well-being. Gender differences are observed, with females and those with a partner reporting higher well-being. In middle-income countries, higher education levels are more linked to loneliness, while in higher-income countries, loneliness is observed across education levels. DISCUSSION AND IMPLICATIONS: Future interventions by governments and policymakers should consider intersectionality in e-Health planning and offer digital literacy and digital skills training to those with lower education levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".