A synthesis of the literature on breaking bad news or truth telling: Potential for research in India
Bibliographic record
Abstract
The high incidence of fatal diseases, inequitable access to health care, and socioeconomic disparities in India generate plentiful clinical bad news including diagnosis of a life-limiting disease, poor prognosis, treatment failure, and impending death. These contexts compel health care professionals to become the messengers of bad news to patients and their families. In global literature on breaking bad news, there is very little about such complex clinical interactions occurring in India or guiding health care providers to do it well. The purpose of this article is to identify the issues for future research that would contribute to the volume, comprehensiveness, and quality of empirical literature on breaking bad news in clinical settings across India. Towards this end, we have synthesized the studies done across the globe on breaking bad news, under four themes: (a) deciding the amount of bad news to deliver; (b) attending to cultural and ethical issues; (c) managing psychological distress; and (d) producing competent messengers of bad news. We believe that robust research is inevitable to build an indigenous knowledge base, enhance communicative competence among health care professionals, and thereby to improve the quality of clinical interactions in India.
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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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".