Professing Professionalism: Are We Our Own Worst Enemy? Faculty Membersʼ Experiences of Teaching and Evaluating Professionalism in Medical Education at One School
Bibliographic record
Abstract
PURPOSE: To explore clinical faculty members' knowledge and attitudes regarding their teaching and evaluation of professionalism. METHOD: Clinical faculty involved in medical education at University of Toronto Faculty of Medicine were recruited to participate in focus groups between 2006 and 2007 to discuss their knowledge, beliefs, and attitudes about teaching and evaluating professionalism and to determine their views regarding faculty development in this area. Focus groups were transcribed, analyzed, and coded for themes using a grounded theory approach. RESULTS: Five focus groups consisting of 14 faculty members from surgical specialties, psychiatry, anesthesia, and pediatrics were conducted. Grounded theory analysis of the 188 pages of text identified three major themes: Professionalism is not a static concept, a gap exists between faculty members' real and ideal experience of teaching professionalism, and "unprofessionalism" is a persistent problem. Important subthemes included the multiple bases that exist for defining professionalism, how professionalism is learned and taught versus how it should be taught, institutional and faculty tolerance and silence regarding unprofessionalism, stress as a contributor to unprofessionalism, and unprofessionalism arising from personality traits. CONCLUSIONS: All faculty expressed that teaching and evaluating professionalism posed a challenge for them. They identified their own lapses in professionalism and their sense of powerlessness and failure to address these with one another as the single greatest barrier to teaching professionalism, given a perceived dominance of role modeling as a teaching tool. Participants had several recommendations for faculty development and acknowledged a need for culture change in teaching hospitals and university departments.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".