Can Teachers Distinguish Competencies From Entrustable Professional Activities?
Bibliographic record
Abstract
INTRODUCTION: There has been a recent transition from the use of "competencies" to "entrustable professional activities" (EPAs) in medical education assessment paradigms. Although this transition proceeds apace, few studies have examined these concepts in a practical context. Our study sought to examine how distinct the concepts of competencies and EPAs were to front-line clinical educators. METHODS: A 20-item survey tool was developed based on the University of Calgary Department of Family Medicine's publicly available lists of competencies and EPAs. This tool required participants to identify given items as either a competency or an EPA, after reading a description of each. The tool was administered to a convenience sample of consenting clinical educators at 5 of the 14 training sites at the University of Toronto Department of Family and Community Medicine in 2018. We also collected information on years in practice, hours spent supervising per week, and direct involvement in medical education. RESULTS: We analyzed a total of 60 surveys. The mean rate of correct responses was 45.3% (+/- 21.8%). Subgroup analysis failed to reveal any correlation between any of the secondary characteristics and correct responses. CONCLUSION: Clinical educators in our study were not able to distinguish between competencies and EPAs. Further research is recommended prior to intensive curricular changes.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".