Abstract 16712: Premedical Students are an Effective and Cost Efficient Way to Teach Large Numbers of People Hands Only CPR in a Community Based Approach
Bibliographic record
Abstract
Introduction: There are approximately 750 cases of cardiac arrest in Jefferson Co, KY each year and only an estimated 6-14% of these individuals survive. The overwhelming death rate can in part be attributed to the low amount of by-stander CPR being performed. A citywide CPR initiative was started to increase the amount of by-stander CPR and ultimately increase the cardiac arrest survival rate. Hypothesis: Premedical students can be trained as hands only CPR instructors and can teach large numbers of people hands only CPR in a community based approach cost effectively. Methods: The program officially launched in the summer of 2014. Over a 12 month period, 12 premedical students were taught to become hands only CPR instructors. These instructors went into high schools, community based organizations and churches to educate individuals on cardiac arrest, hands only CPR, and preventative health measures. Demographic data was collected as well as a pre and post test was performed by each student to gauge the efficacy of the teaching program. Results: During the study period, 7554 people in Louisville, KY participated in the hands only CPR classes. We taught classes in 30 high schools and reached over 90% of all freshmen high school students in Louisville. We taught people in each zip code in Louisville and taught greater than 75 people in 25 of the 35 (70%) zip codes in Louisville. The cost for this program was $3.40/person taught hands only CPR. Conclusions: Using premedical students as hands only CPR instructors is an effective way of teaching large numbers of people hands only CPR in a cost effective manner. This program could be reproduced in other communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.115 | 0.026 |
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".