Predictors of Internet Use Among Older Adults With Diabetes in South Korea: Survey Study
Bibliographic record
Abstract
BACKGROUND: Internet access in Korea has grown dramatically over the past two decades. However, disparities in internet use, referred to as the second level of the digital divide, persist. OBJECTIVE: This study aims to examine opportunity, motivation, and health variables that indicate internet use among older adults with diabetes. METHODS: Data were sourced from a nationally representative sample of people 65 years and older with diabetes (N=1919). Logistic regression was used to explore potential differences in predictor variables between internet users and nonusers. RESULTS: Only 306 of the 1919 (15.95%) participants in the sample used the internet. They were more likely to be younger (odds ratio [OR] 0.89, 95% CI 0.87-0.92), well-educated (OR 1.20, 95% CI 1.16-1.26), and able to afford leisure expenditures (OR 1.02, 95% CI 1.01-1.04). Additionally, they had more information and communications technology (ICT) training experience, were motivated to learn, volunteered, and reported good physical and cognitive function. Participation in ICT education and better health more positively correlated with a higher rate of internet use than did years of education or economic standing in older adults with diabetes. CONCLUSIONS: To support older adults with diabetes in the internet age, policies and health care providers should focus on digital competency training as well as physical and cognitive function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".