Using television shows to teach communication skills in internal medicine residency
Bibliographic record
Abstract
BACKGROUND: To address evidence-based effective communication skills in the formal academic half day curriculum of our core internal medicine residency program, we designed and delivered an interactive session using excerpts taken from medically-themed television shows. METHODS: We selected two excerpts from the television show House, and one from Gray's Anatomy and featured them in conjunction with a brief didactic presentation of the Kalamazoo consensus statement on doctor-patient communication. To assess the efficacy of this approach a set of standardized questions were given to our residents once at the beginning and once at the completion of the session. RESULTS: Our residents indicated that their understanding of an evidence-based model of effective communication such as the Kalamazoo model, and their comfort levels in applying such model in clinical practice increased significantly. Furthermore, residents' understanding levels of the seven essential competencies listed in the Kalamazoo model also improved significantly. Finally, the residents reported that their comfort levels in three challenging clinical scenarios presented to them improved significantly. CONCLUSION: We used popular television shows to teach residents in our core internal medicine residency program about effective communication skills with a focus on the Kalamazoo's model. The results of the subjective assessment of this approach indicated that it was successful in accomplishing our objectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".