Use of simulation in teaching haematological aspects to undergraduate medical students improves student’s knowledge related to the taught theoretical underpinnings
Bibliographic record
Abstract
BACKGROUND: Simulation is an educational method which has several modalities and applications. In the last few decades Simulation-Based Medical Education (SBME) has become a significant influence in medical education. Despite the recognized potential of simulation to be used widely in support of healthcare education, there are no studies focused on the role of simulation in teaching haematology. Moreover, the reaction level is the most commonly reported in medical education. This study evaluates, at two levels of Kirkpatrick's model, the effectiveness of incorporating SBME in teaching haematological aspects to medical students. METHODS: A total of 84 second year medical students from two cohorts received theoretical components of Haematopoietic and Immune System in 4 credits course, delivered using lecture approach. First cohort students (n = 49) participated in interactive learning tutorials to discuss clinical vignettes. Second cohort (n = 35) students participated in simulation sessions where the tutorial's clinical vignettes were developed to clinical simulation scenarios conducted in the simulation centre. The potential influence of the simulation in learning enhancement was evaluated using Kirkpatrick's Evaluation Framework. RESULTS: The students rated the simulation sessions highly and found them to be a valuable learning experience. The category performance summary, generated by the assessment platform, demonstrates improvement in the student's knowledge enhanced by the SBME. CONCLUSIONS: Adaptation of SBME in teaching haematological aspects is a feasible way to improve the student's knowledge related to the taught theoretical foundations. SBME has the potential to enhance the undergraduate medical curriculum and it is expected, in the near future, to be an increasingly recommended educational strategy to bridge the gap between theory and practice.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".