Dynamic deployment models for high-performance Emergency Medical Services
Bibliographic record
Abstract
While the emergency medical services profession has evolved substantially, the way that paramedic resources respond to these incidents has stayed relatively the same, mostly mirroring deployment models utilized by fire departments. The problem is that fire and paramedic services require two very different types of staffing. Fire departments are mainly tasked with protecting property, and therefore follow a static 24/7 deployment model due to predictable demand (PRPS, 2020a). This is not the case however with paramedic services. While fire departments focus on property, paramedics requires a greater focus on protecting people and health. Over the past decade, there has been a staggering increase in medical calls, which has overwhelmed paramedic services across Canada. During the COVID-19 pandemic, most of the world saw ambulance call volumes and response times increase by up to 50% (Amiry & Maguire, 2021), especially for life-threatening emergencies (Prezant et al, 2020). It proves the importance of keeping staffing and deployment planning current to adequately deal with these surges. With regular instances of little to no ambulances available, even on regular days, there need to be improved methods identified for better resource management. For the purposes of this study, the deployment plans at two of Ontario’s largest and busiest paramedic services (referred to as Service A and Service B) were examined, to determine how different deployment models help paramedic services adapt to their call volume and remain prepared for larger-scale emergency responses.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".