Health effects of training laypeople to deliver emergency care in underserviced populations: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: The Disease Control Priorities Project recommends emergency care training for laypersons in low-resource settings, but evidence for these interventions has not yet been systematically reviewed. This review will identify the individual and community health effects of educating laypeople to deliver prehospital emergency care interventions in low-resource settings. METHODS AND ANALYSIS: This systematic review addresses the following question: in underserviced populations and low-resource settings (P), does first aid or emergency care training or education for laypeople (I) confer any individual or community health benefit for emergency health conditions (O), in comparison with no training or other forms of education (C)? We restrict this review to studies reporting quantitatively measurable outcomes, and search 12 electronic bibliographic databases and grey literature sources. A team of expert content and methodology reviewers will conduct title and abstract screening and full-text review, using a custom-built online platform. Two investigators will independently extract methodological variables and outcomes related to patient-level morbidity and mortality and community-level effects on resilience or emergency care capacity. Two investigators will independently assess external validity, selection bias, performance bias, measurement bias, attrition bias and confounding. We will summarise the findings using a narrative approach to highlight similarities and differences between the gathered studies. ETHICS AND DISSEMINATION: Formal ethical approval is not required. RESULTS: The results will be disseminated through a peer-reviewed publication and knowledge translation strategy. REVIEW REGISTRATION NUMBER: CRD42014009685.
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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 | 0.076 | 0.083 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.086 | 0.012 |
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".