Implementing economic evaluation in simulation‐based medical education: challenges and opportunities
Bibliographic record
Abstract
CONTEXT: Simulation-based medical education (SBME) is now ubiquitous at all levels of medical training. Given the substantial resources needed for SBME, economic evaluation of simulation-based programmes or curricula is required to demonstrate whether improvement in trainee performance (knowledge, skills and attitudes) and health outcomes justifies the cost of investment. Current literature evaluating SBME fails to provide consistent and interpretable information on the relative costs and benefits of alternatives. CONTENT: Economic evaluation is widely applied in health care, but is relatively scarce in medical education. Therefore, in this paper, using a focus on SBME, we define economic evaluation, describe the key components, and discuss the challenges associated with conducting an economic evaluation of medical education interventions. As a way forward to the rigorous and state of the art application of economic evaluation in medical education, we outline the steps to gather the necessary information to conduct an economic evaluation of simulation-based education programmes and curricula, and describe the main approaches to conducting an economic evaluation. CONCLUSION: A properly conducted economic evaluation can help stakeholders (i.e., programme directors, policy makers and curriculum designers) to determine the optimal use of resources in selecting the modality or method of assessment in simulation. It also helps inform broader decision making about allocation of scarce resources within an educational programme, as well as between education and clinical care. Economic evaluation in medical education research is still in its infancy, and there is significant potential for state-of-the-art application of these methods in this area.
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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 | 0.542 | 0.744 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".