A framework for residents’ pursuit of excellence based upon non-cognitive and cognitive attributes
Bibliographic record
Abstract
PURPOSE: Excellence, although variably conceptualized, is commonly used in medicine and the resident excellence literature is limited. Both cognitive attributes (CAs) and non-cognitive attributes (NCAs) are essential for academic and clinical performance; however, the latter are difficult to evaluate. Undergirded by an inclusive and non-competitive approach and utilizing CAs and NCAs, we propose a criterion-referenced behavioral framework of resident excellence. METHODS: Perceptions of multiple stakeholders (educational administrators, faculty, and residents), gathered by survey (n = 218), document analysis (n = 52), and focus group (n = 23), were analyzed. Inductive thematic analysis was followed by deductive interpretation and categorization using sensitizing concepts for excellence, NCAs, and CAs. Chi-squared tests were used to determine stakeholder perception differences. RESULTS: All stakeholders had similar perceptions (P > .05) and 13 behavioral attributes in 6 themes undergirded by insight and conscientiousness were identified. The NCAs included: interpersonal skills (works with others, available, humble), professional (compassionate, trustworthy), commitment to profession (visible, volunteers), commitment to learn (proactively seeks feedback, creates learning opportunities), and work-life balance/integration (calm demeanor, inspirational). The CA (medical knowledge and intellect) included: applies knowledge to gain expertise and improves program's caliber. CONCLUSION: Resident excellence is posited as a pursuit. The attributes are non-competitive, inclusionary, potentially achievable by all, and do not negatively affect freedom of choice. However, contextual and cultural differences are likely and these need validation across societal equity segments. There are implications for learners (adaptive reflection and learning goal orientation), faculty (reduced bias and whole-person feedback), and system leaders (enhancing culture and learning environments) to foster excellence.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".