Exploring Spatial Mismatch between Primary Care and Older Populations in an Aging Country: A Case Study of South Korea
Bibliographic record
Abstract
With the rapid growth of aging populations in South Korea, it is important to assess spatial accessibility to healthcare resources as older adults may need frequent visits to hospitals. Healthcare spatial accessibility is measured based on available resources (e.g., physicians, beds, services), demands (e.g., population), and travel costs (e.g., distance or time). In this study, we employed an Enhanced Two-Step Floating Catchment Area (E2SFCA) method to measure the spatial accessibility to primary care for older populations (i.e., aged 65 and older) in major cities in South Korea, including Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan. We found that the aging population in Seoul, the capital and biggest city in South Korea, has relatively better accessibility than those living in other cities. We also discovered a negative relationship between accessibility to primary care and the aging index (i.e., population over 65 years old/population less than 15 years old); the regions with a higher ratio of older populations have lower accessibility to primary care. The results suggested that more primary care services (perhaps via mobile vans) are needed in regions predominantly with older people to improve their healthcare access.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".