Abstract 358: Out-of-hospital Cardiac Arrest and Community First Response: Building Evidence for Policy and Practice
Bibliographic record
Abstract
Introduction: Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality globally. Community First Response (CFR) is an important component of OHCA management in many countries. It entails the mobilisation of volunteers by the Emergency Medical Services to respond to OHCAs in their locality. These volunteers include lay-people and professionals (e.g. nurses, physicians). CFR can increase rates of cardiopulmonary resuscitation or defibrillation performed prior to the Emergency Medical Services’ arrival, though its impact on survival and cognitive function requires further study. This research aimed to improve our understanding of CFR, including volunteers’ motives and activities, the association between volunteer location and social fragmentation/deprivation, and the outcomes that should be measured for this intervention. Methods: This mixed-methods project comprises several key stages: systematic review of the CFR literature, interviews with CFR experts from a range of countries (e.g. USA, Canada, UK, Australia, Singapore), a survey of volunteers, and an analysis of Irish ambulance service records. Results: Various factors affect volunteer motivation, such as personality, family history, legislation, and psychological support. Volunteers undertake many activities in addition to responding to OHCAs, including responding to other emergencies (e.g. stroke), raising awareness of OHCA, providing CPR training, and supporting patients’ relatives. Barriers to responding include problems with technology and recruitment. Volunteer location in Ireland does not appear to be influenced by social fragmentation/deprivation. Outcomes that are measured for CFR include response times and survival. Other potentially important outcomes can prove difficult to measure, particularly the benefits for patients’ relatives and communities. Conclusions: This project has implications for CFR research and practice, especially recruiting and supporting volunteers and measuring outcomes. Improving these processes could help to optimise and build evidence for this intervention. Funding has been obtained to extend this project so that the impact of the coronavirus disease 2019 (COVID-19) pandemic on the CFR evidence base can be examined.
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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 | 0.167 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".