Community Interventions for Out-of-Hospital Cardiac Arrest in Resource-Limited Settings: A Scoping Review Across Low, Middle, and High-Income Countries
Bibliographic record
Abstract
Background Out-of-hospital cardiac arrest (OHCA) is a major global health challenge, characterized by poor survival outcomes worldwide. Resource-limited settings are burdened with suboptimal emergency response and worse outcomes than high-resource areas. Engaging the community in the response to OHCA has the potential to improve outcomes, although an overview of community interventions in resource-limited settings has not been provided.Objective This review evaluated the scope of community-based OHCA interventions in resource-limited settings.Methods Literature searches in electronic databases (MEDLINE, EMBASE, Global Health, CINAHL, Cochrane Central Register of Controlled Clinical Trials) and grey literature sources were performed. Abstract screening, full-text review, and data extraction of eligible studies were conducted independently by two reviewers. The PCC (Population, Concept, and Context) framework was used to assess study eligibility. Studies that evaluated community-based interventions for laypeople (Population), targeting emergency response activation, cardiopulmonary resuscitation (CPR), or automated external defibrillator (AED) use (Concept) in resource-limited settings (Context) were included. Resource-limited settings were identified by financial pressures (low-income or lower-middle-income country, according to World Bank data on year of publication) or geographical factors (setting described using keywords indicative of geographical remoteness in upper-middle-income or high-income country).Results Among 14,810 records identified from literature searches, 60 studies from 28 unique countries were included in this review. Studies were conducted in high-income (n = 35), upper-middle-income (n = 2), lower-middle-income (n = 22), and low-income countries (n = 1). Community interventions included bystander CPR and/or AED training (n = 34), community responder programs (n = 8), drone-delivered AED networks (n = 6), dispatcher-assisted CPR programs (n = 4), regional resuscitation campaigns (n = 3), public access defibrillation programs (n = 3), and crowdsourcing technologies (n = 2). CPR and/or AED training were the only interventions evaluated in low-income, lower-middle-income, and upper-middle-income countries.Conclusions Interventions aimed at improving the community response to OHCA in resource-limited settings differ globally. There is a lack of reported studies from low-income countries and certain continental regions, including South America, Africa, and Oceania. Evaluation of interventions other than CPR and/or AED training in low- and middle-income countries is needed to guide community emergency planning and health policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".