First Things First: Effectiveness and Scalability of a Basic Prehospital Trauma Care Program for Lay First-Responders in Kampala, Uganda
Bibliographic record
Abstract
BACKGROUND: We previously showed that in the absence of a formal emergency system, lay people face a heavy burden of injuries in Kampala, Uganda, and we demonstrated the feasibility of a basic prehospital trauma course for lay people. This study tests the effectiveness of this course and estimates the costs and cost-effectiveness of scaling up this training. METHODS AND FINDINGS: For six months, we prospectively followed 307 trainees (police, taxi drivers, and community leaders) who completed a one-day basic prehospital trauma care program in 2008. Cross-sectional surveys and fund of knowledge tests were used to measure their frequency of skill and supply use, reasons for not providing aid, perceived utility of the course and kit, confidence in using skills, and knowledge of first-aid. We then estimated the cost-effectiveness of scaling up the program. At six months, 188 (62%) of the trainees were followed up. Their knowledge retention remained high or increased. The mean correct score on a basic fund of knowledge test was 92%, up from 86% after initial training (n = 146 pairs, p = 0.0016). 97% of participants had used at least one skill from the course: most commonly haemorrhage control, recovery position and lifting/moving and 96% had used at least one first-aid item. Lack of knowledge was less of a barrier and trainees were significantly more confident in providing first-aid. Based on cost estimates from the World Health Organization, local injury data, and modelling from previous studies, the projected cost of scaling up this program was $0.12 per capita or $25-75 per life year saved. Key limitations of the study include small sample size, possible reporter bias, preliminary local validation of study instruments, and an indirect estimate of mortality reduction. CONCLUSIONS: Lay first-responders effectively retained knowledge on prehospital trauma care and confidently used their first-aid skills and supplies for at least six months. The costs of scaling up this intervention to cover Kampala are very modest. This may be a cost-effective first step toward developing formal emergency services in Uganda other resource-constrained settings. Further research is needed in this critical area of trauma care in low-income countries.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".