Enhancing Medical Learners’ Knowledge of, Comfort and Confidence in Holding Serious Illness Conversations
Bibliographic record
Abstract
Objectives: Having early discussions with seriously ill patients about their priorities and values can improve their peace of mind and family outcomes during bereavement; however, physicians and medical students report feeling underprepared to hold serious illness conversations. We evaluated the impact of the Serious Illness Care Program clinician training workshop on medical learners’ knowledge of comfort and confidence in holding such conversations. Methods: Eligible learners were penultimate- or final-year medical students or first-year residents of generalist programs (Family Medicine, Internal Medicine). Learners participated in a 2.5-hour workshop involving reflection on serious illness discussions, didactic teaching and demonstration of the Serious Illness Conversation Guide (SICG), role play with standardized patients, direct observation, and feedback from experts. Participants completed pre- and post-intervention questionnaires with Likert-type scale and open-ended questions, which were analyzed using paired t tests and qualitative content analysis, respectively. Results: We enrolled 25 learners. The intervention was associated with an increase in knowledge ( P < .001) and self-efficacy ( P < .001). All learners reported gaining new skills, with a majority specifically identifying a framework for structuring serious illness conversations in the qualitative analysis (n = 14, 56%). Participants stated the workshops would improve their comfort in holding serious illness conversations (n = 24, 96%), and that it would be valuable to integrate the workshops into their formal curricula (n = 23, 92%). Conclusions: Training on the use of the SICG is novel for medical students and first-year residents and associated with the improvement in their knowledge of and perceived capacity to hold serious illness conversations. This study suggests that the integration of SICG training into medical curricula may have educational value.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".