Dynamic deployment models for high-performance Emergency Medical Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it