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 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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".