Communicating bad news: attitudes and modes of communication of the health professions
Bibliographic record
Abstract
SUMMARY: TBackground. Information regarding ominous prognoses, which may cause concern and distress, should be provided carefully and cautiously, using non-traumatizing terminology, accommodating the patient's fears, and not excluding elements of hope. Goal. To analyze the difficulties of health care providers in the process of communicating bad news. Materials and Methods. An observational, cross-sectional, multicenter study was conducted from March to August 2021 among Italian Physicians and Nurses. Results. The results of the study indicate a greater participation of Nurse practitioners than Physicians, a fact that may indicate how necessary it is, to overcome the belief that the communication of bad news is of exclusive medical relevance. Among the participants in the study, about half, equal to 46.7% stated that they had no specific training, while the remainder claimed to have attended master's or higher education courses in 8.5% of cases, 23% attended conferences, while 21.8% acquired their skills through work experience. Conclusions. The communication of bad news, needs to be recognized in the same way as those procedures that characterize care itself, and for which the highest possible quality is sought.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".