Barriers to breaking bad news among medical and surgical residents
Bibliographic record
Abstract
UNLABELLED: Communicating "bad news" to patients and their families can be difficult for physicians. OBJECTIVE: This qualitative study aimed to examine residents' perceptions of barriers to delivering bad news to patients and their family members. DESIGN: Two focus groups consisting of first- and second-year medical and surgical residents were conducted to explore residents' perceptions of the bad news delivery process. The grounded theory approach was used to identify common themes and concepts, which included: (1) guidelines to delivering bad news, (2) obstacles to delivering bad news and (3) residents' needs. SETTING: McMaster University, Hamilton, Ontario, Canada. SUBJECTS: First- and second-year residents. RESULTS: Residents were able to identify several guidelines important to communicating the bad news to patients and their family members. However, residents also discussed the barriers that prevented these guidelines from being implemented in day-to-day practice. Specifically, lack of emotional support from health professionals, available time as well as their own personal fears about the delivery process prevented them from being effective in their roles. Residents relayed the need for increased focus on communication skills and frequent feedback with specific emphasis on the delivery of bad news. The residents in our study also stressed the importance of processing their own feelings regarding the delivery process with staff. CONCLUSIONS: Although most residents realize important guidelines in the delivery of bad news, their own fears, a general lack of supervisory support and time constraints form barriers to their effective interaction with patients.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".