Abstract 19639: Reducing Educational Barriers to Address Cpr Training Disparities in a Large Us Urban Community
Bibliographic record
Abstract
Background: Bystander CPR (BCPR) is a crucial therapy for cardiac arrest, yet less than 30%of victims receive it in most communities. Previous research has shown that BCPR training rates vary significantly according to community socioeconomicstatus (SES), with black or Hispanic individuals and those with low medianhousehold incomes being statistically less likely to have CPR training. We sought to test whether a community-based mobile layperson training approach that removed both cost and transportation barriers would target these disparities. Objective: To characterize the impact of our mobile layperson training intervention in a low-SES population through descriptive statistics and choropleth mapping of CPR training activity and associated SES. Design/Methods: A survey study of subjects trained through the mobile training intervention between 03/2013 and 03/2015 in which subjects’ demographics, prior CPR training status, and residential location were collected and analyzed. Subjects’ neighborhood SES at the block group level was ascertained using 2010 United States Census data. Results: Of 5789 subjects trained, 70% completed a demographic survey. 65% were female, 32% identified as white-Hispanic, 43% as black non-Hispanic, and 17% as white non-Hispanic. Of those > 18 years of age, 71% did not complete college. 65% reported never having received CPR training. Attached figure represents the census block groups of the City of Hartford by those trained by the mobile project (A) and associated SES (B). The census block groups with > 90 individuals trained were of low SES. Conclusion: A CPR training approach without transportation or cost barriers was successful in reaching a population of low-SES trainees with little previous exposure to CPR education. This approach holds promise as a scalable method to address SES disparities in BCPR provision.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".