Examining perception and actual knowledge change among learners in a standardized burn course
Bibliographic record
Abstract
B urns are an important public health problem in African and other low-/middle-income countries (LMICs).Africa represents a significant proportion of global burn injury, having the second highest rate of fatal burns worldwide, and is responsible for 15% of global fire-related deaths (1).While the number of burn injuries in high-income countries are decreasing, numbers remain high in LMICs.Nearly 100% of fire-related deaths occur in LMICs, highlighting the fatal consequences of burn injuries in this environment (2,3).While many individuals succumb to burn injuries, a large majority survive.These burn survivors are in need of a health care system that can fully manage this complex problem (4,5).One of the ways to ensure that health care systems can manage the complex needs of burn survivors is through education.Essential Burn Management (EBM) is a burn training program created for East Africa and aims to meet the needs of LMICs.EBM was created in 2005 in conjunction with the Canadian Network for International Surgery (CNIS).The present article highlights the development and ongoing evaluation of this course.We previously reported on whether the course met local needs, and participants' satisfaction with the course (6).We now report on objective testing of change in knowledge, with pre and post testing, and comparison of this testing with the self-perception of knowledge gained to explore the utility of the course.This is the first time that this evaluation of a burn course for LMIC is being reported in the literature. EBM in East Africa: Study location and course developmentOver the past five years, EBM was created and piloted in Ethiopia and Tanzania to address the important need for standardized burn care in a LMIC setting.Course content was based on standard burn teaching for Canadian medical students and residents, and modified through discussion with course participants and burn surgeons in Jimma (Ethiopia) and Dar Es Salaam (Tanzania).Course content and development details are available elsewhere (6).The present article provides findings from the second iteration of EBM that was taught in Dar Es Salaam.Dar es Salaam is Tanzania's largest urban economic centre, with a population of 2.5 million (7).The setting of the course was at Muhimbili National Hospital, which serves as the de facto regional burn unit for Dar Es Salaam.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".