Modeling the Impact of How Volunteer Responders Can Reduce Cardiac Arrest Response Times in Rural Ontario
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».