Abstract 220: Public Defibrillator Accessibility and Mobility Trends During the Covid-19 Pandemic in Canada
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has led to widespread closures of non-essential businesses and buildings. The impact of such closures on public automated external defibrillator (AED) accessibility compared to mobility trends is unknown. Methods: We identified all publicly available online AED registries in Canada last updated May 1, 2019 or later. AEDs were classified by location type using addresses and registry notes, and deemed completely inaccessible, partially inaccessible, or unaffected using government-issued closure orders as of May 1, 2020. We mapped AED location types to categories used by Google’s COVID-19 Community Mobility Reports and calculated the median percent change in daily traffic between Feb. 15 - May 1, 2020 (excluding Apr. 10-12). We compared the percent of completely inaccessible AEDs to the median percent change in traffic for each category. Results: We identified three provincial (British Columbia, Alberta, Nova Scotia) and two municipal (Mississauga and Toronto in Ontario) online AED registries, collectively covering 13.1 million people. Of the 5,845 AEDs identified, 69.9% were completely inaccessible, 18.8% were partially inaccessible, and 11.3% were unaffected. AEDs in parks (n=141), almost all retail and recreational locations (n=1,539), and two-thirds of workplaces (n=3,633) were completely inaccessible, grocery and pharmacy-based (n=173) AEDs were partially inaccessible, and transit station (n=277) and residential (n=85) AEDs were unaffected. The largest discrepancies between AED accessibility and mobility occurred in parks (100% completely inaccessible vs. 10.5% traffic decrease), retail and recreation (99.0% completely inaccessible vs. 48.0% traffic decrease), and transit stations (100% unaffected vs. 63.0% traffic decrease). Conclusion: Government-mandated closures due to the COVID-19 pandemic have led to a greater reduction in AED accessibility than mobility in many locations across Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".