Individual training at the undergraduate level to promote competence in breaking bad news in oncology
Bibliographic record
Abstract
OBJECTIVE: Training medical students in breaking bad news (BBN) in oncology may be key to improve patient care in an area where many physicians tend to be uncomfortable. Given the lack of evidence in the literature, this study aimed to assess empirically the impact of 2 teaching strategies to prepare students for the task of BBN in oncology: one-to-one simulated patient (SP) training with individual feedback (intervention group) vs small-group SP training with collective feedback (comparison group). METHODS: Fourth-year students (N = 236) were randomly assigned to the intervention or comparison group. The SP videotaped interviews were analyzed with respect to BBN communication performance, rated using the Calgary-Cambridge checklist of teaching objectives for BBN; verbal interaction behaviors, coded with the Roter interaction analysis system; and 7 nonverbal behaviors. RESULTS: Students in the intervention group scored significantly higher after than before the training on the overall evaluation of the interview (P < .001) as well as on process skills (P < .001); they also obtained significantly higher scores compared to students in the comparison group on the overall evaluation of the interview (P < .001) and on process skills (P < .001). CONCLUSIONS: This study supports an individualized BBN teaching strategy and contributes to efforts to find the best way to train and reach the largest number of future physicians to improve communication competences in oncology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".