Doctors’ knowledge, practices, challenges, and limitations regarding disclosure of bad news: A multicentre study from Pakistan
Bibliographic record
Abstract
Background: Breaking bad news is one of the most difficult tasks for practicing doctors, especially for those working in health care specialties where life-threatening diseases are diagnosed and managed routinely. Our aim was to elicit the knowledge and practices of doctors and identify barriers faced by them in disclosure of bad news across the provinces of Pakistan. Methods: Cross-sectional, multi-centered study supported by an external grant in 15 Government and Private Hospitals across Pakistan. A total of 1185 doctors were surveyed. Responses were compared across provinces. Results: 80% of doctors across all specialties considered life-threatening diagnoses like cancer and stroke as equivalent to bad news, whereas less than 50% perceived conditions like malaria and typhoid as bad news. Regarding the level of difficulty encountered in giving bad news on a scale of 0 to 6, over 57% doctors rated it 4 and above. The reasons identified were lack of confidentiality, lack of privacy, lack of time, lack of training, fear of patients' and family reactions, not wanting to hurt the patient or causing more distress, concern of having failed the patient, and their own reactions among others. Conclusions: Technical proficiency, training, good patient-centered communication, and incorporating socio-cultural aspects are essential for effective 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".