What Skills Really Improve After a Flipped Educational Intervention to Train Medical Students and Residents to Break Bad News?
Bibliographic record
Abstract
Breaking bad news (BBN) is necessary in medical practice and requires training. The purpose of this study is to evaluate the efficacy and mainly explore the components involved in medical students’ and residents’ performance after a flipped educational intervention to train them to break bad news. A randomized controlled before-after study was conducted with 43 medical students and residents in the intervention group and 41 in the control group. The intervention combined an online multimedia program (DocCom) with a two-hour workshop. BBN performance was assessed at two clinical stations using Objective Structured Clinical Examination and analyzed using a mixed between-within subject analysis of variance. A factor analysis was conducted to analyze the performance by checklist components. The intervention group improved its overall performance in BBN over time (p = 0.000; Eta2 = 0.38) and when compared to the control group (p = 0.01; Eta2 = 0.12). The factor analysis revealed two main components: Factor 1—“giving bad news and responding with empathy”—and Factor 2—“using general communication skills”. Performance analysis by these components revealed that the improvement occurred mainly in Factor 1 (over time, p = 0.000; Eta2 = 0.48, group x time, p = 0.000; Eta2 = 0.38). The intervention combining DocCom Module 33 and a workshop had a moderate effect on the improvement of medical students’ and medical residents’ BBN overall performance in standardized encounters. This improvement was mainly related to communication skills for giving bad news and responding with empathy, in which the intervention effect was large over time and between groups.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".