Toward Authentic Clinical Evaluation: Pitfalls in the Pursuit of Competency
Bibliographic record
Abstract
PURPOSE: The drive toward competency-based education frameworks has created a tension between competing desires-for quantified, standardized measures on one hand, and for an authentic representation of what it means to be a good doctor on the other. The purpose of this study was to better understand the tensions that exist between competency frameworks and faculty's real-life experiences in evaluating residents. METHOD: Interviews were conducted with 19 experienced internal medicine attendings at two Canadian universities in 2007. Attendings each discussed a specific outstanding, average, and problematic resident they had supervised. Interviews were analyzed using grounded theory. RESULTS: Eight major themes emerged reflecting how faculty conceptualize residents' performance: knowledge, professionalism, patient interactions, team interactions, systems, disposition, trust, and impact on staff. Attendings' impressions of residents did not seem to result from a linear sum of dimensions; rather, domains idiosyncratically took on variable degrees of importance depending on the resident. Relative deficiencies in outstanding residents could be overlooked, whereas strengths in problematic residents could be discounted. Some constructs (e.g., impact on staff) were not competencies at all; rather, they seem to act as explanations or evidence of attendings' opinions. Standardized evaluation forms might constrain authentic depictions of residents' performance. CONCLUSIONS: Despite concerted efforts to create standardized, objective, competency-based evaluations, the assessment of residents' clinical performance still has a strong subjective influence. Attendings' holistic impressions should not be considered invalid simply because they are subjective. Instead, assessment methods should consider novel ways of accommodating these impressions to improve evaluation.
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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 | 0.287 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".