The Standardized Live Patient and Mechanical Patient Models—Their Roles in Trauma Teaching
Bibliographic record
Abstract
BACKGROUND: We have previously demonstrated improved medical student performance using standardized live patient models in the Trauma Evaluation and Management (TEAM) program. The trauma manikin has also been offered as an option for teaching trauma skills in this program. In this study, we compare performance using both models. METHODS: Final year medical students were randomly assigned to three groups: group I (n = 22) with neither model, group II (n = 24) with patient model, and group III (n = 24) with mechanical model using the same clinical scenario. All students completed pre-TEAM and post-TEAM multiple choice question (MCQ) exams and an evaluation questionnaire scoring five items on a scale of 1 to 5 with 5 being the highest. The items were objectives were met, knowledge improved, skills improved, overall satisfaction, and course should be mandatory. Students (groups II and III) then switched models, rating preferences in six categories: more challenging, more interesting, more dynamic, more enjoyable learning, more realistic, and overall better model. Scores were analyzed by ANOVA with p < 0.05 being considered statistically significant. RESULTS: All groups had similar scores (means % +/- SD)in the pretest (group I - 50.8 +/- 7.4, group II - 51.3 +/- 6.4, group III - 51.1 +/- 6.6). All groups improved their post-test scores but groups II and III scored higher than group I with no difference in scores between groups II and III (group I - 77.5 +/- 3.8, group II - 84.8 +/- 3.6, group III - 86.3 +/- 3.2). The percent of students scoring 5 in the questionnaire are as follows: objectives met - 100% for all groups; knowledge improved: group I - 91%, group II - 96%, group III - 92%; skills improved: group I - 9%, group II - 83%, group III - 96%; overall satisfaction: group I - 91%, group II - 92%, group III - 92%; should be mandatory: group I - 32%, group II - 96%, group III - 100%. Student preferences (48 students) are as follows: the mechanical model was more challenging (44 of 48); more interesting (40 of 48); more dynamic (46 of 48); more enjoyable (48 of 48); more realistic (32/48), and better overall model (42 of 48). CONCLUSIONS: Using the TEAM program, we have demonstrated that improvement in knowledge and skills are equally enhanced by using mechanical or patient models in trauma teaching. However, students overwhelmingly preferred the mechanical model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".