Communication Training at Medical School: A Quantitative Analysis
Bibliographic record
Abstract
Background: There is an increasing focus on communication between doctors and patients, and recent systematic reviews argue that teaching doctors necessary communication skills benefits patients at large. Moreover, patients report the lack of communication as their second-leading complaint. Motivational interviewing has proved to be person-centered in healthcare in communication between patients and doctors. Aim: To examine how being inspired by motivational interviewing theory and using the Calgary Cambridge guide could improve medical students' communication skills at the master level using a mixed-method approach. Methods: A cohort study with an exposed cohort compared to a non-exposed historical cohort. The participants were students in their sixth year of medical training from the Clinical Department of the University of Southern Denmark. The non-exposed cohort received laboratory training based on the Calgary Cambridge Guide. After this training, they participated in a two-month clinical "stay" and recorded two digital audio files of a real conversation with a patient about delivering information. The exposed cohort followed the same schedule but received additional special training in MI. All audio files were analyzed using the Motivational Interviewing Integrity method (MITI). An additional focus group interview was conducted to support the results. Results: Medical students demonstrated improvements in several essential areas of their communication style favorable to the MI approach, particularly empathy, and person-centeredness. The focus group interviews supported these findings.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".