Short Survey on Cardiopulmonary Resuscitation and Automated External Defibrillator Training in Rural British Columbia Schools: Preliminary Findings and Hypothesis-Generating Insights
Bibliographic record
Abstract
Background British Columbia (BC) faces over 7,000 out-of-hospital cardiac arrests (OHCA) annually, disproportionately affecting rural areas due to slower emergency medical service (EMS) response and limited specialized care. Despite known benefits of automated external defibrillator (AED) access and cardiopulmonary resuscitation (CPR) training, their status in rural BC schools is poorly documented. Methods We used an online survey with principals and vice-principals of rural schools in BC. The survey assessed AED accessibility, prevalence of CPR and AED training and obstacles to implementing such training. Questions covered school demographics, AED installation, and CPR/AED training for staff and students. Results We recruited 23 (46%) elementary (kindergarten - grade 7), 6 (12%) middle (grades 6 - 8), and 21 (42%) high schools (grades 8 - 12). 72% (36 out of 50) had at least one AED installed, 46% required staff CPR training, and 24% provided student CPR training. Significant gaps in training were noted for elementary and middle school students compared to high schools (p<0.05). Conclusions Disparities in AED and CPR training across rural schools in BC exist, highlighting a need for policy improvements and innovative solutions to enhance first aid education. Barriers to implementing CPR and AED training included lack of funding, curricular priority, time constraints, and limited resources. Despite a 10.3% response rate, this study reveals significant disparities in AED and CPR training across school levels in rural BC, underscoring the need for targeted policies and educational strategies to enhance emergency preparedness and improve cardiac arrest outcomes in underserved areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".