Access to Emergency Medical Services: An Urban Planning Methodology for the Generation of Equity
Bibliographic record
Abstract
The access and possibility of rapid response to medical emergencies is an issue that, in last decades, has been studied in many areas of research such as urban planning and transportation, as well as with the issue of equity in the provision of this healthcare service. This is true, in particular for middle- and low-income countries subject to non-equitative access to services such as medical emergency attention. In this study, a medical emergency database review is carried out in order to propose a new methodology to assess the coverage of Ambulance Dispatches and Emergency Service Facilities. This is executed using primary information relating to medical emergencies that occurred in the city of Manizales between 2010 and 2015 and secondary data relating to socio-demographic and economic conditions, analyzing their correlation using a GIS (Geographic Information System) application. This research article proposes a methodology for improving the population coverage of Ambulance Dispatches and Emergency Service Facilities, trying to reduce health inequity in terms of assistance to medical emergencies. Our results show that in order to reduce social and health inequity, the city of Manizales needs to improve its provision of emergency care attention, based on urban planning tools especially in low-income neighborhoods.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".