Putting the Horse Back in Front: Advancing Simulation Education With Implementation Science and Structured Research Processes
Bibliographic record
Abstract
This editorial explores the application of implementation science methodologies within simulation-based health professions education. It introduces two models, the adapted implementation model for simulation (AIM-SIM) and the implementation quality rubric for simulation (IQR-SIM), tailored to optimize educational simulation programs' development, implementation, and long-term sustainability in simulation contexts. These models are introduced against the backdrop of their development process, which notably lacked a formal needs assessment, highlighting a critical gap in their foundational preparation. To address this gap effectively, the editorial advocates for a scoping review as a strategic next step. The proposed scoping review will aim to comprehensively survey the landscape of existing literature, specifically probing the utilization of implementation science methodologies within simulation-based education. By identifying gaps and assessing the current state of research, the proposed scoping review will seek to substantiate the necessity for a simulation-specific model grounded in implementation science principles. The outcomes of the future scoping review are anticipated to validate the applicability and relevance of AIM-SIM and IQR-SIM in real-world educational settings. Moreover, it may provide insights crucial for refining these models to better meet the dynamic and nuanced needs of the field. By systematically scrutinizing the existing literature, the proposed scoping review may also elude to how effectively current methodologies address the complexities of simulation-based education. Ultimately, this process has the potential to inform future directions in research and practice, ensuring that simulation programs are not only effectively implemented but also sustained over time, thereby maximizing their impact on health professions education.
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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 | 0.108 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".