Using a Scenario-Based Approach to Teaching Professionalism to Medical Students: Course Description and Evaluation
Bibliographic record
Abstract
BACKGROUND: Doctors play a key role in individuals' lives undergoing a holistic integration into local communities. To maintain public trust, it is essential that professional values are upheld by both doctors and medical students. We aimed to ensure that students appreciated these professional obligations during the 3-year science-based, preclinical course with limited patient contact. OBJECTIVE: We developed a short scenario-based approach to teaching professionalism to first-year students undertaking a medical course with a 3-year science-based, preclinical component. We aimed to evaluate, both quantitatively and qualitatively, student perceptions of the experience and impact of the course. METHODS: An interactive professionalism course entitled Entry to the Profession was designed for preclinical first-year medical students. Two scenario-based sessions were created and evaluated using established professionalism guidance and expert consensus. Quantitative and qualitative feedback on course implementation and development of professionalism were gathered using Likert-type 5-point scales and debrief following course completion. RESULTS: A total of 70 students completed the Entry to the Profession course over a 2-year period. Feedback regarding session materials and logistics ranged from 4.16 (SD 0.93; appropriateness of scenarios) to 4.66 (SD 0.61; environment of sessions). Feedback pertaining to professionalism knowledge and behaviors ranged from 3.11 (SD 0.99; need for professionalism) to 4.78 (SD 0.42; relevance of professionalism). Qualitative feedback revealed that a small group format in a relaxed, open environment facilitated discussion of the major concepts of professionalism. CONCLUSIONS: Entry to the Profession employed an innovative approach to introducing first-year medical students to complex professionalism concepts. Future longitudinal investigations should aim to explore its impact at various stages of preclinical, clinical, and postgraduate 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".