Trauma Training Courses and Programs in Low‐ and Lower Middle‐Income Countries: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Injury is the leading cause of morbidity and mortality in low- and lower middle-income countries (LMICs). Trauma training is a cost-effective way to improve injury outcomes. Several trauma programs have been implemented in LMICs; however, their scope and effectiveness remain unclear. In this review, we sought to describe and assess the current state of trauma training in LMICs. METHODS: We searched MEDLINE, Embase, Global Health, Cochrane Library, and ProQuest Dissertations & Theses Global for trauma training courses in LMICs. An additional gray literature search was conducted on university, governmental, and non- governmental organizations' websites to identify trauma-related postgraduate medical education (PGME) opportunities. RESULTS: Most studies occurred in sub-Saharan Africa and participants were primarily physicians/surgeons, medical students/residents, and nurses. General and surgical trauma management courses were most common, followed by orthopedic trauma or plastic surgery trauma/burn care courses. 32/45 studies reported on participant knowledge and skills, 27 of which had minimal follow-up. Of the four studies commenting on cost of courses, only one demonstrated cost-effectiveness. Three articles evaluated post-course effects on patient outcomes, two of which failed to demonstrate significant improvements. Overall, 43.0% of LMICs have PGME programs with defined trauma competency requirements. CONCLUSIONS: Current studies on trauma training in LMICs do not clearly demonstrate sustainability, cost-effectiveness, nor improved outcomes. Trauma training programs should be in response to a need, championed locally, and work within a cohesive system to demonstrate concrete benefits. We recommend standardized and contextualized trauma training with recertifications in LMICs for lasting and improved trauma care.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 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".