A Dialogic Approach to Teaching Person-Centered Care in Graduate Medical Education
Bibliographic record
Abstract
BACKGROUND: Training future physicians to provide compassionate, equitable, person-centered care remains a challenge for medical educators. Dialogues offer an opportunity to extend person-centered education into clinical care. In contrast to discussions, dialogues encourage the sharing of authority, expertise, and perspectives to promote new ways of understanding oneself and the world. The best methods for implementing dialogic teaching in graduate medical education have not been identified. OBJECTIVE: We developed and implemented a co-constructed faculty development program to promote dialogic teaching and learning in graduate medical education. METHODS: Beginning in April 2017, we co-constructed, with a pilot working group (PWG) of physician teachers, ways to prepare for and implement dialogic teaching in clinical settings. We kept detailed implementation notes and interviewed PWG members. Data were iteratively co-analyzed using a qualitative description approach within a constructivist paradigm. Ongoing analysis informed iterative changes to the faculty development program and dialogic education model. Patient and learner advisers provided practical guidance. RESULTS: The concepts and practice of dialogic teaching resonated with PWG members. However, they indicated that dialogic teaching was easier to learn about than to implement, citing insufficient time, lack of space, and other structural issues as barriers. Patient and learner advisers provided insights that deepened design, implementation, and eventual evaluation of the education model by sharing experiences related to person-centered care. CONCLUSIONS: While PWG members found that the faculty development program supported the implementation of dialogic teaching, successfully enabling this approach requires expertise, willingness, and support to teach knowledge and skills not traditionally included in medical curricula.
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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 | 0.034 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".