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 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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".