Simulation for Health Professionals Learning How to Deliver Bad News: A Rapid Review of the Literature
Bibliographic record
Abstract
Background: Delivering bad news is part and parcel of the practice of numerous health care professionals. The educational activities offered to help develop communication skills in this regard are limited, but simulation remains an often-used option. The purpose of this rapid review was to describe the features of the simulation activities available to health care professionals seeking to learn how to deliver bad news. Method: A rapid review of the literature was conducted by referring to the MEDLINE and CINAHL databases. The characteristics of the studies conducted, the features of the simulation activities, and the learning outcomes were extracted and analyzed by relying on the New World Kirkpatrick Model’s levels of evaluation. The results obtained are presented in both graphs and tables. Results: A total of 14 articles were analyzed. The majority of educational activities using simulation as a teaching strategy generated positive learning outcomes with regard to delivering bad news. A marked improvement in communication skills was noted, along with greater confidence in applying the communication techniques taught. Various teaching methods rely on SPIKES and SHARE models as theoretical foundations and incorporate post-simulation debriefing. Pedagogical activities, such as conferences or group discussions, are often recommended prior to or after a simulation exercise. Conclusion: The characteristics of the available educational activities vary greatly, and further studies will be needed to evaluate the impact of specific characteristics on how health care professionals learn to deliver bad news. Further research will also be necessary with regard to the actual impact of this learning in the health care professionals’ work setting and on the persons who receive the bad news.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".