Bibliographic record
Abstract
In Reply to Kelly et al: We thank Kelly and colleagues for their comments on our article. The authors called attention to 2 salient strategies—regular formal evaluator discussions and synthetic assessment frameworks that hold potential to foster fairness and objectivity in current clerkship evaluation systems. Regular formal evaluator discussions provide an effective form of frame-of-reference training (FORT) for faculty evaluators to hone validity and reliability in workplace assessment.1 A recent study on FORT showed that quality and specificity of narrative comments and entrustment ratings in workplace-based assessments improved when faculty participated in FORT sessions.2 In addition, qualitative analysis of such sessions was reviewed positively by faculty, citing the benefits of practicing and improving discrimination of students between skill levels.3 Although our article discussed the use of entrustable professional activities (EPAs) as a potential solution, we agree that assessment frameworks, like the reporter-interpreter-manager-educator (RIME) method, may provide utility. RIME allows graders to accurately depict a learner’s progression from gathering information and interpreting medical decision-making data to teaching others. There are few studies evaluating RIME’s usefulness across clerkships. One such study by Ryan and colleagues investigated the validity of RIME across all clerkships at a single academic institution. The authors reported that RIME was an effective and reliable tool for clinical clerkships, but observed variance due to factors outside of the learner and limitations caused by small number of assessments.4 We also want to highlight the findings of Imm and colleagues who developed a composite work-based performance tool that combined frameworks of EPAs, RIME, and Modified Ottawa Co-Activity and trialed it in assessments of third-year medical students across multiple clerkships.5 Ratings were found to be consistent across diverse clerkships and reliably reflected learner growth as students increased clinical exposure and engaged in higher complexity tasks.5 We appreciate the insights provided by Kelly and colleagues. As highlighted by all of these studies, there remains much needed research and discussion on solutions to address widespread discrepancies in current clerkship evaluation systems. Jeffry Nahmias, MD, MHPEProfessor and division chief, Division of Trauma, Burns, and Critical Care Surgery, University of California, Irvine, School of Medicine, Irvine, California; email: [email protected]Ashley HuynhSecond-year medical student, University of California, Irvine, School of Medicine, Irvine, CaliforniaChristian de Virgilio, MDProfessor and chair, Department of Surgery, Harbor-UCLA Medical Center, Torrance, California
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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 | 0.006 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.037 | 0.048 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
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".