Assessing Professionalism: A Theoretical Framework for Defining Clinical Rotation Assessment Criteria
Bibliographic record
Abstract
Although widely accepted as an important graduate competence, professionalism is a challenging outcome to define and assess. Clinical rotations provide an excellent opportunity to develop student professionalism through the use of experiential learning and effective feedback, but without appropriate theoretical frameworks, clinical teachers may find it difficult to identify appropriate learning outcomes. The adage "I know it when I see it" is unhelpful in providing feedback and guidance for student improvement, and criteria that are more specifically defined would help students direct their own development. This study sought first to identify how clinical faculty in one institution currently assess professionalism, using retrospective analysis of material obtained in undergraduate teaching and faculty development sessions. Subsequently, a faculty workshop was held in which a round-table type discussion sought to develop these ideas and identify how professionalism assessment could be improved. The output of this session was a theoretical framework for teaching and assessing professionalism, providing example assessment criteria and ideas for clinical teaching. This includes categories such as client and colleague interaction, respect and trust, recognition of limitations, and understanding of different professional identities. Each category includes detailed descriptions of the knowledge, skills, and behaviors expected of students in these areas. The criteria were determined by engaging faculty in the development of the framework, and therefore they should represent a focused development of criteria already used to assess professionalism, and not a novel and unfamiliar set of assessment guidelines. The faculty-led nature of this framework is expected to facilitate implementation in clinical teaching.
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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 | 0.056 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".