Preparedness, Challenges, and Opportunities for Digital Intervention for Chronic Disease Management: A Qualitative Study in Rural Areas of South Korea
Bibliographic record
Abstract
Motivated by the prevalence of an aging population and the associated increase in chronic diseases, coupled with rising medical expenditure, the Korean government initiated a pilot project in Pyeongchang-gun, Gangwon-do, a rural area, to implement a "smart online-to-offline (O2O) digital health care model" aimed at managing and preventing chronic diseases. However, there is limited understanding regarding perspectives and levels of preparedness for digital health among stakeholders at various levels. In-depth focus group interviews were conducted with elderly and non-elderly community members, health care providers, and staff members at Pyeongchang Health and Medical Center. The study found the presence of both positive and negative perceptions and a lack of preparedness across different levels. At the end-user level, it was observed that community members, especially the elderly, have low levels of health and digital literacy, compounded by limited access to social support. At the health care provider level, there was uncertainty about the acceptance of the digital health program. At the area level, the need to bolster health staff members and enhance their capacity was observed. Recommendations include: customizing the design of the online and offline service components by considering end-user factors (such as age, occupation, and household type) that may contribute to disparities in health; establishing a platform for providers to share their experiences to facilitate the effective incorporation of digital health into their practices; and preparing an appropriate provider payment mechanism.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".