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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| 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".