Why Should Implementation Science Matter in Simulation-based Health Professions Education?
Bibliographic record
Abstract
Simulation is a key contributor to quality medical education. However, results achieved when simulation programs are developed and tested in controlled experimental settings do not automatically translate into improved learner outcomes when these programs are implemented in real-world settings. Although over the last decade implementation science has emerged as a field intended to guide the implementation of evidence-based programs in various contexts, recent reviews suggest that it has not been integrated into simulation-based education. Implementation science is defined as a rigorous study of methods that allow for a systematic uptake of research findings and other evidence-based practices. The ultimate goal of implementation science is to provide an evidence-based approach to program delivery in practice in order to achieve the targeted health and education outcomes and maximize the return on research investments. The main reason is that in order to achieve the intended outcomes, it is crucial to pay attention to both program quality and implementation quality. In other words, having a good simulation program does not guarantee achieving the intended learning outcomes. In this editorial we propose to highlight the research to practice gap in simulation-based health professions education, introduce the concept of implementation science and how it can serve to close the gap, and provide an example of a model derived entirely based on other models and frameworks existing in the field of implementation science to help simulation program directors and other administrators to implement simulation programs into educational 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 | 0.039 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".