Development of professionalism vignettes for the continuum of learners within a medical and nursing community of practice
Bibliographic record
Abstract
BACKGROUND: It is challenging to develop professionalism curricula for all members of a medical community of practice. We collected and developed professionalism vignettes for an interactive professionalism curriculum around our institutional professionalism norms following social constructivist learning theory principles. METHODS: Medical students, residents, physicians, nurses and research team members provided real-life professionalism vignettes. We collected stories about professionalism framed within the categories of our Faculty's code of conduct: honesty; confidentiality; respect; responsibility; and excellence. Altruism was from the Nursing Code of Ethics. Two expert committees anonymously rated and then discussed vignettes on their educational value and degree of unprofessional behaviour. Through consensus, the research team finalized vignette selection. RESULTS: Eighty cases were submitted: 22 from another study; 20 from learners and nurses; and 30 from physicians; and eight from research team members. Two expert committees reviewed 53 and 42 vignettes, respectively. The final 18 were selected based upon: educational value; diversity in professionalism ratings; and representation of the professionalism categories. CONCLUSION: Realistic and relevant professionalism vignettes can be systematically gathered from a community of practice and their representation of an institutional norm, educational value, and level of professional behaviour can be judged by experts with a high level of consensus.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".