Current status of cardiopulmonary resuscitation training and automatic external defibrillator availability in high schools in Halifax, Nova Scotia, Canada
Bibliographic record
Abstract
Background: School-based cardiopulmonary resuscitation (CPR) training and early use of an automated external defibrillator (AED) have proven to increase the survival of victims of sudden cardiac arrest (SCA). This study aimed to determine the status of CPR training, availability of AEDs, and medical emergency response programs (MERPs) in high schools in Halifax Regional Municipality. Method: High school principals were asked to participate in a voluntary online survey comprising questions about demographics, AEDs availability, CPR training for staff and students, the existence of MERPs, and perceived barriers. Three autogenerated reminders followed the initial invitation. Results: Out of 51 schools, 21 (41%) responded, only 10% (2/21) and 33% (7/21) reported providing CPR training to students and staff, respectively. About 35% (7/20) of the schools reported having AEDs, but only 10% (2/20) have MERPs for SCA. All respondents reported in favor of AED availability in schools. The reported barriers to CPR training included limited financial resources (54%), perception of low priority (23%), and time constraints (23%). Respondents reported limited financial resources (85%) and the lack of trained staff to use (30%) as the main reasons for the unavailability of AEDs. Conclusion: This survey showed that all respondents overwhelmingly favour having access to AEDs. However, the availability of CPR and AED training for staff and students in schools remains inadequate. Emergency action plans have not been devised, and few schools have AED devices. More education and awareness are needed to ensure lifesaving equipment and practices in all Halifax Regional Municipality schools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".