Testing the ABCs of Serious Illness Program for Oncology Trainees: A Feasibility Trial Comparing Different Learning Formats for a Virtual Communication Curriculum
Bibliographic record
Abstract
Background: ABCs (All providers, Better Communication skills) of serious illness communication is a novel curriculum that could enhance postgraduate oncology training. The program combines electronic learning modules (ELMs), standardized patient (SP) encounters, and coaching, all in a virtual format. We assessed feasibility of a randomized controlled trial (RCT) comparing different training experiences. Methods: We conducted a pilot for a RCT at three academic centers. Postgraduate oncology trainees were randomized to complete ELMs and SP encounters with coaching (intervention arm) versus ELMs only (control arm). Feasibility measures comprised the primary analysis. Secondary analyses explored the impact of different training experiences. Outcomes were measured through pre- and post-intervention simulation-based assessments (COM-ON rating scale), and surveys rating self-efficacy (End-of-Life Professional Caregiver Survey [EPCS] score) and satisfaction. Results: Twenty-three learners participated (37% recruitment). Adherence was 100% and data collection was near complete. All feasibility metrics were met except for the recruitment target of 75%. Self-efficacy ratings improved from baseline (EPCS score increased significantly ( p < 0.001), mean paired difference = 0.73 (standard deviation [SD] = 0.78) [95% confidence interval (CI), 0.37–1.08]), as did quality of communication in simulations (COM-ON score increased significantly ( p < 0.001), mean paired difference = 0.59 (SD = 0.73) [95% CI, 0.28–0.91]). Improvement was greatest in the intervention arm for both. Participants reported high satisfaction with virtual learning. Conclusions: Recruitment was below target, but study activities were feasible in virtual format. The curriculum improved communication skills. The addition of virtual SPs and coaching optimized learning. The curriculum was associated with improved self-efficacy rating for serious illness communication with oncology patients. Lessons learned will support further medical education research in this area.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".