Current Patterns of Prehospital Trauma Care in Kampala, Uganda and the Feasibility of a Lay‐First‐Responder Training Program
Bibliographic record
Abstract
BACKGROUND: Uganda currently has no organized prehospital emergency system. We sought to measure the current burden of injury seen by lay people in Kampala, Uganda and to determine the feasibility of a lay first-responder training program. METHODS: We conducted a cross-sectional survey of current prehospital care providers in Kampala: police officers, minibus taxi drivers, and Local Council officials, and collected data on types and frequencies of emergencies witnessed, barriers to aid provision, history of training, and current availability of first-aid supplies. A context-appropriate course on basic first-aid for trauma was designed and implemented. We measured changes in trainees' fund of knowledge before and after training. RESULTS: A total of 309 lay people participated in the study, and during the previous 6 months saw 18 traumatic emergencies each; 39% saw an injury-related death. The most common injury mechanisms were road crashes, assault, and burns. In these cases, 90% of trainees provided some aid, most commonly lifting (82%) or transport (76%). Fifty-two percent of trainees had previous first-aid training, 44% had some access to equipment, and 32% had ever purchased a first-aid kit. Before training, participants answered 45% of test questions correctly (mean %) and this increased to 86% after training (p < 0.0001). CONCLUSIONS: Lay people witness many emergencies and deaths in Kampala, Uganda and provide much needed care but are ill-prepared to do so. A context-appropriate prehospital trauma care course can be developed and improve lay people's knowledge of basic trauma care. The effectiveness of such a training program needs to be evaluated prospectively.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".