Marrying Content and Process in Clinical Method Teaching
Bibliographic record
Abstract
Communication skills training is now internationally accepted as an essential component of medical education. However, learners and teachers in communication skills programs continue to experience problems integrating communication with other clinical skills, ensuring that clinical faculty support and teach communication beyond the formal communication course, extending communication training coherently into clerkship and residency, and applying communication skills in medical practice at a professional level of competence. One factor contributing to these problems is that learners confront two apparently conflicting models of the medical interview: a communication model describing the process of the interview and the "traditional medical history" describing the content of the interview. The resulting confusion exacerbates the above dilemmas and interferes with learners using communication skills training to advantage in real-life practice. The authors propose a comprehensive clinical method that explicitly integrates traditional clinical method with effective communication skills. To implement this more comprehensive approach, they have modified their own Calgary-Cambridge guides to the medical interview by developing three diagrams that visually and conceptually improve the way communication skills teaching is introduced and that place communication process skills within a comprehensive clinical method; devising a content guide for medical interviewing that is more closely aligned with the structure and process skills used in communication skills training; and incorporating patient-centered medicine into both process and content aspects of the medical interview. These enhancements help resolve ongoing difficulties associated with both teaching communication skills and applying them effectively in medical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".