Abstract 10861: Determining the Importance of Gender, Race, and Body Shape for Cardiopulmonary Resuscitation Education Using Manikins
Bibliographic record
Abstract
Introduction: Cardiopulmonary resuscitation (CPR) education plays a critical role in reducing cardiovascular deaths. Thus, it is crucial that trainees engage in practice simulations that accurately replicate a cardiovascular emergency. Recent work has shown that certain demographic groups such as Black Americans and women are less likely to receive efficient CPR, and technique may vary on individuals who have an increased body mass index or who are pregnant. Poor representation of these groups in the manikins used for CPR simulation may play a substantial role in these inequities. Hypothesis: There is a deficiency in the diversity of race, gender, and body habitus of manikins used for CPR training in North and South America. Methods: Institutions, businesses, and non-governmental organizations which administer CPR certification in North and South America were identified for survey distribution through a collaboration with the Inter-American Society of Cardiology. A survey was administered using the online platform Qualtrics (Provo, USA) consisting of 18 questions about manikin supply, usage, and diversity. Results and Conclusions: A total of 52 survey responses were received from North America (n=20; 854 total manikins) and South America (n=32; 1,169 total manikins). Of the total manikins (n=2,023), 318 (16%) were non-white, 114 (6%) were female, 20 (1%) represented a non-lean body habitus, and 18 (1%) were pregnant. The importance of diverse manikin representation in simulation training is underscored by literature detailing deficiencies in CPR initiation and associated outcomes in individuals who are not lean white males. Yet, the majority of manikins used in North and South America still disproportionally represent this population. It is pertinent that manikins used for CPR training reflect all populations at risk for major adverse cardiovascular events.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".