Evaluation of a High Stakes Physician Competency Assessment: Lessons for Assessor Training, Program Accountability, and Continuous Improvement
Bibliographic record
Abstract
INTRODUCTION: There is a dearth of evidence evaluating postlicensure high-stakes physician competency assessment programs. Our purpose was to contribute to this evidence by evaluating a high-stakes assessment for assessor inter-rater reliability and the relationship between performance on individual assessment components and overall performance. We did so to determine if the assessment tools identify specific competency needs of the assessed physicians and contribute to our understanding of physician dyscompetence more broadly. METHOD: Four assessors independently reviewed 102 video-recorded assessments and scored physicians on seven assessment components and overall performance. Inter-rater reliability was measured using intraclass correlation coefficients using a multiple rater, consistency, two-way random effect model. Analysis of variance with least-significant difference post-hoc analyses examined if the mean component scores differed significantly by quartile ranges of overall performance. Linear regression analysis determined the extent to which each component score was associated with overall performance. RESULTS: Intraclass correlation coefficients ranged between 0.756 and 0.876 for all components scored and was highest for overall performance. Regression indicated that individual component scores were positively associated with overall performance. Levels of variation in component scores were significantly different across quartile ranges with higher variability in poorer performers. DISCUSSION: High-stake assessments can be conducted reliably and identify performance gaps of potentially dyscompetent physicians. Physicians who performed well tended to do so in all aspects evaluated, whereas those who performed poorly demonstrated areas of strength and weakness. Understanding that dyscompetence rarely means a complete or catastrophic lapse competence is vital to understanding how educational needs change through a physician's career.
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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 | 0.104 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".