Examining perception and actual knowledge change among learners in a standardized burn course
Bibliographic record
Abstract
Background Essential Burn Management (EBM) is a burn training program created for East Africa and aims to meet the needs of low- and middle-income countries. The authors present a report on objective testing of change in knowledge, with pre and post tests, and comparison of this testing with the self perception of knowledge gained to explore course utility. Objective To evaluate the ability of EBM to improve knowledge in burn care among course participants; and to explore whether participants' self-perception of knowledge gained is comparable with their actual change in knowledge. Methods Twenty health care providers from a variety of disciplines participated in EBM and completed the pre and post course burn care knowledge test. Participants also self-rated knowledge in burn care both pre and post course. All tests and self-rated assessments were anonymous and consent was obtained. Paired t tests were conducted on pre and post test scores. Improvements in pre-post scores according to module or topic area were examined to determine the proportion correct, and then analyzed using Wilcoxon signed-rank tests. Module scores pre-post course were compared with individuals' self-rating of knowledge both before and after the course for that particular module. Results Pre-post course tests reflected an increase in knowledge. An increase in self-rated knowledge was matched with a significant increase in module test scores for primary survey, burn resuscitation, wound management, infection control and electrical injury, but not inhalation injury and compartment syndrome modules. Conclusion Findings support a combination of self-report and objective pre-post testing to evaluate courses designed to teach burn management.
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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 | 0.016 |
| 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) | 0.002 | 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".