A Question of Scale? Generalizability of the Ottawa and Chen Scales to Render Entrustment Decisions for the Core EPAs in the Workplace
Bibliographic record
Abstract
PURPOSE: Assessments of the Core Entrustable Professional Activities (Core EPAs) are based on observations of supervisors throughout a medical student's progression toward entrustment. The purpose of this study was to compare generalizability of scores from 2 entrustment scales: the Ottawa Surgical Competency Operating Room Evaluation (Ottawa) scale and an undergraduate medical education supervisory scale proposed by Chen and colleagues (Chen). A secondary aim was to determine the impact of frequent assessors on generalizability of the data. METHOD: For academic year 2019-2020, the Virginia Commonwealth University School of Medicine modified a previously described workplace-based assessment (WBA) system developed to provide feedback for the Core EPAs across clerkships. The WBA scored students' performance using both Ottawa and Chen scales. Generalizability (G) and decision (D) studies were performed using an unbalanced random-effects model to determine the reliability of each scale. Secondary G- and D-studies explored whether faculty who rated more than 5 students demonstrated better reliability. The Phi-coefficient was used to estimate reliability; a cutoff of at least 0.70 was used to conduct D-studies. RESULTS: Using the Ottawa scale, variability attributable to the student ranged from 0.8% to 6.5%. For the Chen scale, student variability ranged from 1.8% to 7.1%. This indicates the majority of variation was due to the rater (42.8%-61.3%) and other unexplained factors. Between 28 and 127 assessments were required to obtain a Phi-coefficient of 0.70. For 2 EPAs, using faculty who frequently assessed the EPA improved generalizability, requiring only 5 and 13 assessments for the Chen scale. CONCLUSIONS: Both scales performed poorly in terms of learner-attributed variance, with some improvement in 2 EPAs when considering only frequent assessors using the Chen scale. Based on these findings in conjunction with prior evidence, the authors provide a root cause analysis highlighting challenges with WBAs for Core EPAs.
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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 | 0.095 | 0.291 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".