Accessibility Assessment of Prehospital Emergency Medical Services considering Supply-Demand Differences
Bibliographic record
Abstract
The reasonable accessibility assessment method is an important basis for the measurement of the level of prehospital emergency medical services. There is no general model for prehospital emergency care in traditional accessibility evaluation, and its supply-demand characteristics have also been ignored. Based on the three-step floating catchment area (3SFCA) model, the supply-demand three-step floating catchment area (SD3SFCA) model is proposed in this paper, which can express the difference between supply and demand of prehospital emergency medical services and accurately simulate unified dispatching of emergency centers. The unified dispatching behavior of emergency centers is simulated based on the potential service capacity of emergency stations with a supply-demand difference. The supply capacity of different emergency facilities is quantified from the perspective of infrastructure and technical quality. The needs of typical population densities are taken into account and adjusted by the weighting index. The validity of the model is verified, with the prehospital emergency medical service in the West Coast New District of Qingdao as an example. The results show that the model can effectively measure the accessibility level of prehospital emergency services and truly reflect the characteristics of supply and demand. Compared with previous models, the model has been significantly improved, which can provide an important reference for optimizing the allocation of prehospital emergency resources.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".