Modeling the Impact of How Volunteer Responders Can Reduce Cardiac Arrest Response Times in Rural Ontario
Bibliographic record
Abstract
Out-of-hospital cardiac arrest (OHCA) is one of the most time-critical emergencies, with survival dropping 7–10% for every minute without defibrillation. Emergency medical services (EMS) are often delayed in rural regions, limiting survival. Community First Responders (CFRs), trained volunteers activated through smartphone applications, offer a potential way to bridge this gap. This project evaluates how CFR recruitment could reduce response times in Kingston and Frontenac County, aligning with the “Neighbours Saving Neighbours” initiative. Spatial modeling was conducted in QGIS using a gridded approach to study the relationship between responder distribution and response times. The study area was divided into 5 × 5 km squares (25 km² each), with each cell representing a potential recruitment zone. Two maps were generated: one showing historical OHCA incidence hotspots, and another showing EMS median response times, which were faster in Kingston and slower in northern rural areas. Squares with no OHCAs (grey cells) were excluded. To refine the model, 5 × 5 km cells were later subdivided into 1 × 1 km cells and merged with census data to explore how population density might guide recruitment. Simulations were run by randomly placing responders in the grid while systematically increasing the number of volunteers in increments of 4 (from 0 up to 40). Under assumptions of a 0.5 alert acceptance rate and a 0.5 travel success rate, outcomes included median response time (the “typical” case where half of OHCAs were reached faster and half slower), 90th percentile response time (the longest 10% of cases), and the share of OHCAs reached within 6, 10, and 15 minutes. Results show that median response time decreased from ~9 minutes to ~7 minutes, while the 90th percentile improved from ~16 to ~13 minutes. Early coverage improved most sharply: the proportion of OHCAs reached within 6 minutes increased from ~2% to over 35%, while 10-minute coverage rose from ~50% to nearly 70%. Fifteen-minute coverage plateaued near 100%, indicating diminishing returns once the volunteer pool exceeded ~30. This grid-based modeling demonstrates that even modest CFR recruitment significantly shifts the response time distribution, particularly in rural areas where EMS travel times are longest. By showing where cardiac arrests occur, how fast current response times are, and how many responders are required per grid square, this framework highlights the potential of CFR expansion. Findings suggest that scaling initiatives like “Neighbours Saving Neighbours” could meaningfully improve the chance of survival in Kingston.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".