Competency-based simulation education: should competency standards apply for simulation educators?
Bibliographic record
Abstract
The healthcare education landscape is evolving. Recent years have seen a change in conceptualisations of learning, assessment and time-based versus competency-based education (CBE).1 These changes will influence healthcare provider training and ultimately the clinical care they provide to patients. CBE has elevated the discourse surrounding clinical competencies and entrustable professional activities.2 Inherent to this focus on educational outcomes is a renewed attention on the role of formative clinical experiences: how we engage and empower learners in their own education; how we organise workplace-based learning to provide the graded supervision our trainees require while maintaining patient safety; and how we help our trainees maximise learning from clinical practice and progress in their training through robust assessment and feedback mechanisms.2 ,3 This changing landscape places a heavy burden on busy clinician educators who themselves may require significant training and faculty development to translate the emerging educational science into effective clinical teaching practice. The very nature of CBE requires clinical educators to assess learners frequently in a manner that allows reliable and valid inferences across the spectrum of clinical competencies that are required for their specific training programme.1 In addition, assessment of individual learner competencies will occur in workplace settings where clinical care is a team activity. We see a mismatch between a CBE approach we value and strive for and the relative underemphasis of faculty teaching skills required for its effective implementation and outcomes assessment. Without equal and parallel attention to clinical educator training, we fear this disconnect has the potential to undermine the translation of promising advances gleaned from healthcare education research into widespread clinical education. The increasing adoption of healthcare …
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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 | 0.067 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".