Teaching residents to put patients first: creation and evaluation of a comprehensive curriculum in patient-centered communication
Bibliographic record
Abstract
Abstract Background Patient-centered communication is essential for successful patient encounters and positive patient outcomes. Therefore, training residents how to communicate well is one of the key responsibilities of residency programs. However, many residents, especially international medical graduates, continue to struggle with communication barriers. Methods All residents and faculty from a small community teaching hospital participated in a three-year, multidimensional patient-centered communication curriculum including communication training with lectures, experiential learning, communication skills practice, and reflection in the areas of linguistics, physician-patient communication, cultural & linguistically appropriate care, and professionalism. We evaluated the program through a multipronged outcomes assessment, including self-assessment, scores on the Calgary-Cambridge Scale during Objective Structured Clinical Examination (OSCE), a survey to measure the hidden curriculum, English Communication Assessment Profile (E-CAP),, the Maslach Burnout-Inventory (MBI), and residents’ evaluation of faculty communication. Results Sixty-two residents and ten faculty members completed the three-year curriculum. We saw no significant changes in the MBI or hidden curriculum survey. Communication skills as measured by Calgary Cambridge Score, E-CAP, and resident communication improved significantly (average Calgary-Cambridge Scale scores from 70% at baseline to 78% at follow-up (p-value < 0.001), paired t-test score from 68% at baseline to 81% at follow-up (p-value < 0.004), average E-CAP score from 73 to 77% (p-value < 0.001)). Faculty communication and teaching as rated by residents also showed significant improvement in four out of six domains (learning climate (p < 0.001), patient-centered care (p = 0.01), evaluation (p = 0.03), and self-directed learning (p = 0.03)). Conclusion Implementing a multidimensional curriculum in patient-centered communication led to modest improvements in patient-centered communication, improved language skills, and improved communication skills among residents and faculty.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".