Using “Standardized Narratives” to Explore New Ways to Represent Faculty Opinions of Resident Performance
Bibliographic record
Abstract
PURPOSE: Most efforts to develop reliable evaluations of clinical competence have been oriented toward deconstructing the requisite competencies into separate scales. However, many are questioning the value of this approach on theoretical and empirical bases. This study uses "standardized narratives" to explore a different approach to assessing resident performance. METHOD: In 2009, based on interviews with 19 experienced clinical faculty from two institutions, 16 narrative profiles were created to represent the range of resident competence that clinical faculty might encounter during supervision. Fourteen clinicians from three institutions independently grouped the profiles into as many categories as necessary to reflect various levels of performance, described their categories, then ranked the individual profiles within each category. Then, in groups of three or four, participants negotiated a final ranking and grouping of the 16 profiles. RESULTS: Despite interesting idiosyncracies in the factors some participants identified as guiding their rankings, there was strong consistency across the 14 clinicians regarding the rankings (single-rater intraclass correlation [ICC] = 0.86) and groupings (single-rater ICC = 0.81) of the profiles. Similarly, across institutions, the four groups were highly consistent in their final negotiated rankings (single-group ICC = 0.91) and groupings (single-group ICC = 0.87) of the profiles. CONCLUSIONS: Faculty showed more consistency in their decisions of what constitutes excellent, competent, and problematic performance in residents than implied by current assessment techniques that require deconstruction of resident competencies. This use of standardized narratives points to interesting opportunities for more authentically codifying faculty opinions of residents.
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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 | 0.031 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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 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".