Genetic simulation for high‐stakes conversations
Bibliographic record
Abstract
BACKGROUND: High-stakes conversations are frequent in Medical Genetics. News shared is often perceived as "bad" and can lead to patient hostility. Breaking bad news (BBN) is therefore a challenging clinical task for physicians and is often included as a foundational skill in medical education. The methods of teaching this skill are variable, with no widely accepted standard. We propose the use of simulation as a safe and effective training tool. APPROACH: Medical Genetics residents participated in a 4-week curriculum on BBN and de-escalating patient hostility and anger. The curriculum consisted of (1) a standardised patient simulation scenario requiring the disclosure of abnormal prenatal test result to a hostile patient, (2) coaching and feedback by genetic counsellors (GCs), (3) reflective exercises, and (4) workshops on de-escalation techniques. Trainees completed a postsimulation survey and postencounter reflection forms. Written comments on the survey and the reflections were analysed for themes. EVALUATION: Six junior and four senior residents participated in this curriculum innovation. Analysis of reflections revealed that simulation coupled with the genetic counsellor's (GC) timely feedback and reflection exercise were good education strategies for practicing BBN and de-escalation techniques in a challenging counselling situation. Most of the trainees felt that this teaching approach was successful and should be used for future training. IMPLICATIONS: Simulation can help prepare Medical Genetics trainees deliver difficult news and successfully de-escalate a hostile patient encounter. Consideration should be given to counselling and de-escalation simulations as a useful addition to standing curricula for Medical Genetics trainees.
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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 | 0.005 | 0.021 |
| 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 | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".