Prehospital Emergency Care in Low- and Middle-Income Countries: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: An under-developed and fragmented prehospital Emergency Medical Services (EMS) system is a major obstacle to the timely care of emergency patients. Insufficient emphasis on prehospital emergency systems in low- and middle-income countries (LMICs) currently causes a substantial number of avoidable deaths from time-sensitive illnesses, highlighting a critical need for improved prehospital emergency care systems. Therefore, this systematic review aimed to assess the prehospital emergency care services across LMICs. METHODS: This systematic review used four electronic databases, namely: PubMed/MEDLINE, CINAHL, EMBASE, and SCOPUS, to search for published reports on prehospital emergency medical care in LMICs. Only peer-reviewed studies published in English language from January 1, 2010 through November 1, 2022 were included in the review. The Newcastle-Ottawa Scale (NOS) and Critical Appraisal Skills Programme (CASP) checklist were used to assess the methodological quality of the included studies. Further, the protocol of this systematic review has been registered on the International Prospective Register of Systematic Reviews (PROSPERO) database (Ref: CRD42022371936) and has been conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Of the 4,909 identified studies, a total of 87 studies met the inclusion criteria and were therefore included in the review. Prehospital emergency care structure, transport care, prehospital times, health outcomes, quality of information exchange, and patient satisfaction were the most reported outcomes in the considered studies. CONCLUSIONS: The prehospital care system in LMICs is fragmented and uncoordinated, lacking trained medical personnel and first responders, inadequate basic materials, and substandard infrastructure.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".