The McGill World Restart a Heart 2020 Campaign: a student-led, patient-engaged, large-scale online simulation program
Bibliographic record
Abstract
Abstract Background Cardiac arrest is a leading cause of death, with the majority of cases occurring in homes or public places. Up to 55% are witnessed by family members, colleagues or friends, yet most victims don't receive cardiopulmonary resuscitation (CPR), which can triple the odds of survival. Mortality therefore remains high at over 90%. Enhancing bystander CPR skills is a crucial public health investment. Objective Engage the Canadian and global community in a thought-provoking and immersive experience to explore cardiac arrests, destigmatize bystander CPR, and enhance resuscitation knowledge and skills. Partner with patients, leverage innovative technologies, and social media, to develop a digital scalable simulation/educational exercise. Methods Interprofessional healthcare students from the Faculty coordinated a University World Restart a Heart (WRAH) 2020 Campaign as part of a global initiative. Amplified by social media influencers, the Campaign team produced a bilingual digital Campaign with succinct and high-impact videos including survivor and non-survivor family perspectives, digital live events, a patient education guide for families of cardiac arrest patients, and an open access eModule. Our Steering Committee comprised University leadership, intensive and emergency care physicians, a cardiac arrest survivor, and the communications team from the Faculty, Centre for Simulation and Interactive Learning, and University Health Centre. Results The Campaign garnered an international audience with over 74,636 impressions across social media platforms (Facebook, Instagram, and Twitter), 42,600 views of Campaign videos, and 1,500 views of hour-long webinars. Campaign ambassadors had a combined viewership of over 500,000 followers, and included astronaut physicians, Olympic athletes, adult and pediatric cardiac arrest survivors, and social media influencers. The Campaign launched an innovative bystander resuscitation eModule that delivers scalable screen-based simulation. The patient education guide, in collaboration with the University Health Centre's Patient Education Office, focuses on the chain of survival and expectations for acute management, hospitalization and recovery. Conclusion Tens of thousands of individuals reached through the University WRAH 2020 Campaign are now empowered to initiate CPR, which may help reduce the morbidity and mortality of cardiac arrests. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): The McGill Fund.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".