Medical students′ and postgraduate residents′ observations of professionalism
Bibliographic record
Abstract
BACKGROUND: There is increasing interest in teaching professionalism to medical learners. The purpose of this study was to explore professionalism observed among medical learners and faculty in a Canadian academic institution. METHODS: A total of 253 medical learners (30% response rate) completed an online survey measuring medical professionalism. The survey used a validated professionalism scale "Climate of Professionalism", which queries subjects' observations of professional and unprofessional behavior in clinical teaching environments. RESULTS: Overall, 73.3% of medical learners felt prepared in the area of medical professionalism. Differences existed in observed professionalism by level of training. By respondents' reports, both medical students and residents viewed their peer groups as more professional than the other. Both groups also rated faculty as the poorest in terms of observed professional behaviors but the best in observed unprofessional behavior. DISCUSSION: Most learners in this Canadian medical school felt well prepared in the area of professionalism, and each training level viewed their peer group as the most professional. Peer groups may rate themselves more favorably due to increased interaction with their group, and active recall of professional communications. This study found differences in observations of professionalism by training level, therefore provides support for specialized professionalism education tailored to the learners level of medical training.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".