Pilot Study: Moving Towards a Scalable Intervention for Postgraduate Communication Skills Training
Bibliographic record
Abstract
Background: Communication skills are foundational to the practice of medicine and training to build them is recommended. Serious illness communication skills (SICSs) teaching is inconsistently and sparsely taught in postgraduate training and residents report feeling inadequately trained to have difficult conversations. The authors developed an e-module demonstrating high-yield communication skills from a known evidence-based training program to standardize core SICS teaching and questioned how using it before skills practice impacted comfort and preparedness for residents to complete advance care planning (ACP). Methods: Family medicine residents at an academic hospital in Toronto, Canada, completed a novel e-module that replaced a typical didactic-lecture introducing core SICS relevant to ACP conversations. Residents then discussed the skills, followed by practicing them deliberately in a structured role-play simulation with feedback by trained facilitators. Residents completed pre- and post-intervention attitudinal surveys. Results: Residents preferred a combination of learning modalities and welcomed online and virtual teaching methods for learning SICS. Residents reported higher levels of preparedness for engaging in ACP, delivering serious news, and discussing goals of care post-intervention. Residents showed more interest in discussing ACP post-intervention but questioned feasibility for doing so in busy ambulatory clinics. Conclusion: Scalable time-efficient teaching strategies are needed to fill a known education gap. This study demonstrated benefits of incorporating brief e-module learning into residents' preparation for SICS training using deliberate practice simulation training. The online, interactive virtual training improved resident readiness and comfort for ACP, an area often overlooked in medical education. Moreover, it provides an evidence-informed standardized tool for clinician teachers to seamlessly incorporate into their teaching practices.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".