An Interprofessional Approach to Teaching Communication Skills
Bibliographic record
Abstract
INTRODUCTION: Recent research suggests that effective interprofessional communication and collaboration can positively influence patient satisfaction and outcomes. Health professional communication skills do not necessarily improve over time but can improve with formal communication skills training (CST). This article describes the development, evaluation, and lessons learned for a novel theater-based role-play CST program designed to improve community cancer care for patients and families by enhancing health care professionals' communication skills. INTERVENTION: Four 2-hour interprofessional communication skills workshops for Nova Scotia health professionals were developed. Topics were (1) Essential Communication Skills, (2) Delivering Difficult News and Providing Support, (3) When Patients and Families Are Angry, and (4) Managing Conflict in the Workplace. Strategies for enhancing communication skills based on the science (evidence-based practice and teaching) and the art (interactive theater) of communication skills were included. Facilitators included professional actors, communication skills facilitators, and trained health professionals. EVALUATION: We used a mixed-methods evaluation design assessing 4 levels of educational outcomes at 3 points: pre- and post-workshop and follow-up. RESULTS: Five hundred eighteen professionals representing over 20 health professions attended 17 workshops. Data showed the workshops were well received, despite some discomfort with role-playing. Pre/post paired t-tests of self-reported communication skills showed significant improvement after all workshops (p ≤ 0.05); 92% indicated intended changes to their communication practice immediately following the workshops. Of 68 respondents to the follow-up, 59 (87%) reported positive changes in the responses of their patients. DISCUSSION: Both positive and negative lessons learned are described.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".