Teaching and assessing intra-operative consultations in competency-based medical education: development of a workplace-based assessment instrument
Bibliographic record
Abstract
Competency-based medical education (CBME) is being implemented worldwide. In CMBE, residency training is designed around competencies required for unsupervised practice and use entrustable professional activities (EPAs) as workplace "units of assessment". Well-designed workplace-based assessment (WBA) tools are required to document competence of trainees in authentic clinical environments. In this study, we developed a WBA instrument to assess residents' performance of intra-operative pathology consultations and conducted a validity investigation. The entrustment-aligned pathology assessment instrument for intra-operative consultations (EPA-IC) was developed through a national iterative consultation and used clinical supervisors to assess residents' performance at an anatomical pathology program. Psychometric analyses and focus groups were conducted to explore the sources of evidence using modern validity theory: content, response process, internal structure, relations to other variables, and consequences of assessment. The content was considered appropriate, the assessment was feasible and acceptable by residents and supervisors, and it had a positive educational impact by improving performance of intra-operative consultations and feedback to learners. The results had low reliability, which seemed to be related to assessment biases, and supervisors were reluctant to fully entrust trainees due to cultural issues. With CBME implementation, new workplace-based assessment tools are needed in pathology. In this study, we showcased the development of the first instrument for assessing resident's performance of a prototypical entrustable professional activity in pathology using modern education principles and validity theory.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".