From Bricks to Buildings
Bibliographic record
Abstract
SUMMARY STATEMENT: Presently, health care simulation research is largely conducted on a study-by-study basis. Although such "project-based" research generates a plethora of evidence, it can be chaotic and contradictory. A move toward sustained, thematic, theory-based programs of research is necessary to advance knowledge in the field. Recognizing that simulation is a complex intervention, we present a framework for developing research programs in simulation-based education adapted from the Medical Research Council (MRC) guidance. This framework calls for an iterative approach to developing, refining, evaluating, and implementing simulation interventions. The adapted framework guidance emphasizes: (1) identification of theory and existing evidence; (2) modeling and piloting interventions to clarify active ingredients and identify mechanisms linking the context, intervention, and outcomes; and (3) evaluation of intervention processes and outcomes in both the laboratory and real-world setting. The proposed framework will aid simulation researchers in developing more robust interventions that optimize simulation-based education and advance our understanding of simulation pedagogy.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.153 | 0.046 |
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".