Competencies “Plus”: The Nature of Written Comments on Internal Medicine Residentsʼ Evaluation Forms
Bibliographic record
Abstract
BACKGROUND: Comments on residents' in-training evaluation reports (ITERs) may be more useful than scores in identifying trainees in difficulty. However, little is known about the nature of comments written by internal medicine faculty on residents' ITERs. METHOD: Comments on 1,770 ITERs (from 180 residents in postgraduate years 1-3) were analyzed using constructivist grounded theory beginning with an existing framework. RESULTS: Ninety-three percent of ITERs contained comments, which were frequently easy to map onto traditional competencies, such as knowledge base (n = 1,075 comments) to the CanMEDs Medical Expert role. Many comments, however, could be linked to several overlapping competencies. Also common were comments completely unrelated to competencies, for instance, the resident's impact on staff (813), or personality issues (450). Residents' "trajectory" was a major theme (performance in relation to expected norms [494], improvement seen [286], or future predictions [286]). CONCLUSIONS: Faculty's assessments of residents are underpinned by factors related and unrelated to traditional competencies. Future evaluations should attempt to capture these holistic, integrated impressions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".