A Study on the Actual Condition of Ophthalmic Medical Institutions in Korea
Bibliographic record
Abstract
Purpose: Approximately 90% of the medical institutions in Korea are located in cities, so there is a disparity in medical resource distribution between urban and rural areas. In order to establish an equal healthcare system, it is necessary to understand the distribution and treatments offered by medical institutions and to investigate access to these facilities. In the present study, we investigated medical institutions offering ophthalmic examinations for children in Korea.Methods: The presence or absence of regional eye clinics and ophthalmologists in public health centers, public health offices, and military medical facilities were investigated in different cities and counties. In addition, the population status and ophthalmic facilities in vulnerable areas were investigated.Results: In the second quarter of 2021, there were 1,658 regional eye clinics in Korea located in Seoul, Gyeonggi, Busan, and Daegu, respectively. There were a total of 3,610 ophthalmologists in Seoul, Gyeonggi, Busan, and Daegu, respectively. Among the 250 counties, 20 did not have eye clinics while 13 did not have an ophthalmologist. The average time required to reach the closest eye examination center was 48.0 ± 38.1 minutes by car and 75.1 ± 40.0 minutes by public transportation. The total population in vulnerable areas was 558,336, including 28,358 children under the age of 10 years.Conclusions: The present study identified vulnerable areas for eye examinations. Based on the findings, it is necessary to establish a healthcare system with improved accessibility, equity, and efficiency considering the importance of ophthalmic examinations in children and the elderly.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".