An integrated analysis of spatial access to the three-tier healthcare delivery system in China: a case study of Hainan Island
Bibliographic record
Abstract
BACKGROUND: Access to healthcare is critical for the implementation of Universal Health Coverage. With the development of healthcare insurance systems around the world, spatial impedance to healthcare institutions has attracted increasing attention. However, most spatial access methodologies have been developed in Western countries, whose healthcare systems are different from those in Low- and Middle-Income Countries (LMICs). METHODS: Hainan Island was taken as an example to explore the utilization of modern spatial access techniques under China's specialized Three-Tier Health Care Delivery System. Healthcare institutions were first classified according to the three tiers. Then shortest travel time was calculated for each institution's tier, overlapped to identify eight types of multilevel healthcare access zones. Spatial access to doctors based on the Enhanced Two-Step Floating Catchment Area Method was also calculated. RESULTS: On Hainan Island, about 90% of the population lived within a 60-min service range for Tier 3 (hospital) healthcare institutions, 80% lived within 30 min of Tier 2 (health centers), and 75% lived within 15 min of Tier 1 (clinics). Based on local policy, 76.36% of the population living in 48.52% of the area were able to receive timely services at all tiers of healthcare institutions. The weighted average access to doctors was 2.31 per thousand residents, but the regional disparity was large, with 64.66% being contributed by Tier 3 healthcare institutions. CONCLUSION: Spatial access to healthcare institutions on Hainan Island was generally good according to travel time and general abundance of doctors, but inequity between regions and imbalance between different healthcare institution tiers exist. Primary healthcare institutions, especially in Tier 2, should be strengthened.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".