The role of family and culture in the disclosure of bad news: A multicentre cross-sectional study in Pakistan
Bibliographic record
Abstract
Objectives: Disclosure of bad news is distressing for patients and family members. Our aim was to assess patients' perceptions and preferences regarding bad news in the health setting. Methods: Cross-sectional, multi-centered study supported by an external grant in 15 Government and Private Hospitals across Pakistan. A sample size of 1673 patients and family members was used. Ethics permission/consent was taken from each participating hospital and participant. Responses were compared across provinces, gender, age, education and income. Results: >80% patients preferred their relatives to know the diagnosis first and they wanted the news to be disclosed to them by doctors. Significant association between education level, income and preference for wanting to know the diagnosis was found. Reasons for wanting to know the diagnosis included treatment, prognosis and prevention options whereas reasons for not wanting to know included fear of emotions and God's will. Conclusion: The majority of Pakistani patients want to be informed and want the family to know first. Preferences for disclosure vary across, age, education and income level. Innovation: First countrywide study on this topic. Identifies need for culturally sensitive guidelines that include the family's role in disclosure of bad news.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".