Senior Residentsʼ Views on the Meaning of Professionalism and How They Learn about It
Bibliographic record
Abstract
PURPOSE: To determine senior residents' views on the meaning of professionalism and how they learned about it. METHOD: By means of a modified Dillman technique, all senior residents at two faculties of medicine (n = 533) were surveyed about professionalism during the 1998-99 academic year. The residents were asked to list attributes of professionalism and to rank methods they found most useful for learning about professionalism, to rate the adequacy and quality of their teaching about professionalism and their comfort in explaining the concept of professionalism to a more junior trainee, to list suggestions about how teaching about professionalism could be improved, and to name the medical organization most concerned with matters of professionalism. RESULTS: A total of 258 residents (48.4%) responded. They listed 1,052 attributes they associated with professionalism. The three most common attributes, all listed by more than 100 respondents, were respect, competence, and empathy. The respondents had learned the most about professionalism from observing role models, they rated the quantity and quality of teaching about it positively, and they felt comfortable explaining professionalism to a junior resident. Only 56% of the residents correctly identified the Canadian medical organization most concerned with professionalism. CONCLUSION: Residents' knowledge about professionalism reflects their early stage of development as physicians and their daily activities, where such aspects of professionalism as the social contract, codes of ethics, participation in professional societies, and altruism are not highlighted. Residency programs should develop teaching activities focusing on professionalism that relate to issues residents face in their daily work.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".