A focus on the burdens, boosters, and bonuses of the bearers of bad news in India
Bibliographic record
Abstract
Objectives: This study was done to explore the experiences of physicians in India about being the messengers of bad news and management of psychosocial burdens associated with such consultations.Methods: Narrative data was collected from 27 physicians working in four teaching hospitals, using a semi-structured interview schedule.Constant comparison analytic procedures were used to examine physicians' perceptions and behaviors related to their role as the bearers of bad news.Results: Physicians perceived that being a messenger of bad news was very challenging throughout the course of their careers, although their self-confidence increased over time.Two types of patient care contexts were identified based on the intensity and duration of distress experienced by the physicians.Treatment failure with children and young adults, patients' inability to access care at the initial stages of the disease, and withdrawal of life-saving treatments due to financial constrains caused intense distress among physicians.Physicians used a number of strategies to cope with the burden of bearing bad news.Clinical bad news puts physicians at risk for burnout, and in some cases is an opportunity for growth.Conclusions: Clinical skill trainings should increase clinicians' ability to assess and attend to the psychosocial impacts of delivering bad news as much as teaching them the procedures of conveying such information.More studies about the impacts of bad news disclosure on physicians working in societies or settings with inequitable access to health care will improve such training programs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".