“You Want Me to Assess What?”: Faculty Perceptions of Assessing Residents From Outside Their Specialty
Bibliographic record
Abstract
PROBLEM: Competency-based medical education (CBME) demands that residents be directly observed performing clinical tasks; however, many faculty lack assessment expertise, and some programs lack resources and faculty numbers to fulfill CBME's mandate. To maximize limited faculty resources, the authors explored training and deploying faculty to assess residents in specialties outside their own. APPROACH: In spring 2017, 10 MD and 2 PhD assessors at a medium-sized medical school in Ontario, Canada, participated in a 4-hour training session, which focused on providing formative assessments of patient handover, a core competency of medical practice. Assessors were deployed to 2 clinical settings outside their own specialty-critical care and pediatrics-each completing 11 to 26 assessments of residents delivering patient handover. Assessors were subsequently interviewed regarding their experiences. OUTCOMES: While assessors felt able to judge handover performance outside their specialty, their sense of comfort varied with their own prior experiences in the given settings. Lack of familiarity with the process of handover in a specific setting directly influenced assessors' perceptions of their own credibility. Although assessors identified the potential benefits of cross-specialty assessment, they also cited challenges to sustaining this approach. NEXT STEPS: Findings indicate a possible "contextual threshold" for cross-specialty assessment: tasks with high context specificity might not be suitable for cross-specialty assessment. Introducing higher-fidelity simulation into the training protocol and ensuring faculty members are remunerated for their time are necessary to establish future opportunities for shared assessment resources across training programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".