Mindful medical practice: An innovative core course to prepare medical students for clerkship
Bibliographic record
Abstract
BACKGROUND: Medical students show a decline in empathy and ethical reasoning during medical school that is most marked during clerkship. We believe that part of the problem is that students do not have the skills and ways of being and relating necessary to deal effectively with the overwhelming clinical experience of clerkship. APPROACH: At McGill University in Montreal, starting in January 2015, we have taught a course on mindful medical practice that combines a clinical focus on the combination of mindfulness and congruent relating that is aimed at giving students the skills and ways of being to function effectively in clerkship. The course is taught to all medical students in groups of 20, weekly for 7 weeks, in the 6 months immediately prior to clerkship, a time when students are very open to learning the skills they need to take effective care of patients. EVALUATION: The course has been well accepted by students as evidenced by their engagement, their evaluations, and their comments in the essays that they write at the end of the course. In a follow-up session at the simulation centre one year later students remember clearly and enact what they were taught in the course. REFLECTION: The next steps will be to conduct a formal evaluation of the effect of our teaching that will involve a combination of qualitative methods to clarify the nature of the impact on our students and a quantitative assessment of the difference the course makes to students' experience and performance in clerkship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".