Roads less traveled: understanding the “why” in simulation as an integrated continuing professional development activity
Bibliographic record
Abstract
BACKGROUND: The simulation community has experienced significant advances, strengthening the case for the use of simulation in medical education toward improving patient outcomes. However, an underlying assumption particularly regarding utilization of simulation by those who are in practice, is that simulation will be selected as a continuing professional development (CPD) strategy. Exploring reasons for choices of educational formats, particularly regarding simulation, is poorly integrated into CPD research. DISCUSSION: Despite significant advances the scientific simulation community has been slow to produce evidence regarding why practitioners may be reserved in engaging in simulation or not. Using examples from related education contexts the author attempts to bridge simulation science, CPD and less commonly used theoretical frameworks to address this issue. The author argues that theoretical perspectives that recognize the use of simulation for CPD as a socio-personal process and/or a personal or group issue (e.g., theories of intelligence, self-determination theory, theory of planned behavior, social identity theory) and that are conceptually distinct from educational mechanisms/ provision are necessary to advance simulation use in CPD contexts. CONCLUSION: Given the close relationship practicing clinicians have to patient outcomes a new imperative may be to focus on the theoretical and practical links informing simulation use for CPD at the level of the individual and individual-among-professional groups. The simulation community may therefore need to engage in research that attempts to further uncover and address underlying issues of "why" clinicians integrate simulation as CPD activities or not.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| 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".