Evaluating spatial accessibility to healthcare services from the lens of emergency hospital visits based on floating car data
Bibliographic record
Abstract
Public medical facilities that are closely related to the health of residents have been recognised as one of the most crucial elements in sustainable urban planning. For the sake of social equality of medical services (especially for emergency medical conditions), the spatial distributions of medical resources need to be accurately measured and continuously optimized. This study presents an effective method to examine night emergency hospital visit and analyse its spatiotemporal characteristics using float car data (FCD). By extracting the hospital service areas, the two-step floating catchment area (2SFCA) methodology was improved to calculate hospital accessibility. Then, the balance between hospital accessibility and population density was analysed. In addition, we investigated the relationship between individual hospital choice preferences and hospital level and analysed several factors that affect individual choices. These results help us understand the special requirements and need of emergency hospital travel in cities and identify areas where medical resources are scarce. They can be used as guidance for urban hospital planning and construction. And the approach of hospital access behaviour investigation and the improved 2SFCA method can also provide insights for other activity-based travel behaviour research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".