E-Module Learning for Scaling Serious Illness Communication Skills Teaching: A Pilot Study in Family Medicine and Palliative Care
Bibliographic record
Abstract
Background: Serious illness communication (SIC) competency is essential for health care professionals. However, many clinicians receive little-to-no SIC training, and there is little evidence as to which teaching method is most feasible to incorporate into postgraduate curricula. Two e-modules were created to adapt high-yield knowledge to deliver asynchronous, time-efficient, standardized communication skills teaching. This project evaluated SIC e-module teaching feasibility, learner and faculty perceptions toward e-module learning on this topic, as well as learner confidence and skill usage post-completion. Methods: Family Medicine residents and palliative care fellows from two training sites were invited to asynchronously complete the e-modules on their own time and complete a survey to assess attitudes, perceptions, and needs toward them and impact on SIC skills immediately and 1-month post-completion. Faculty from the main site were also invited to view the e-modules and complete a survey immediately afterward assessing attitudes, perceptions, and feasibility on SIC e-module learning. Results: In total, 19/50 (38%) learners completed the e-modules and post-training survey and 14/19 (73%) of those learners completed the 1-month follow-up survey. In total, 13/60 (22%) faculty completed the survey. Participants liked the structure and design of the e-modules and felt they were appropriate for their learners' level of training, were effective, time-efficient, and provided relevant SIC information. Case-based video demonstrations were identified as the most useful teaching method. Most learners intended to use new skills in clinical practice, rewatched both e-modules within 1 month of initial viewing, and reported using learned skills in practice. Conclusion: E-module training provides a standardized method to scale postgraduate SIC skills teaching asynchronously and was well liked by learners and faculty. Barriers exist to completing them outside of a core curriculum. Early data suggest e-modules can be used iteratively and further research is needed to determine how their use impacts communication confidence and competency.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".