Cultural Differences in Perceived Appropriateness of Breaking Bad News to Patients: A Direct Comparison of Togo and France
Bibliographic record
Abstract
We examined cross-cultural differences in people’s positions regarding the appropriateness of breaking of bad news to elderly patients. A total of 450 Togolese and French people who had in the past received bad medical news were presented with 72 vignettes depicting communication of bad news to elderly female patients and asked to indicate the appropriateness of physicians’ conduct in each case. The vignettes comprised five pieces of information: (a) the severity of the disease, (b) the patient’s wishes, (c) the level of social support during hospitalization, (d) the patient’s psychological robustness, and (e) the physician’s decision about communicating bad news. Through cluster analysis, six qualitatively different positions were found: (a) Always Tell the Truth to Patients, (b) Tell the Truth to Patients or their Relatives, (c) Depends on Patients’ Wishes, (d) Tell the Truth to the Relatives, (e) Don’t Tell the Truth to Patients, and (f) Undetermined. The French participants reported a stronger tendency to endorse the view that physicians should always tell the truth directly to the patient than the Togolese participants. In contrast, there was a stronger tendency among the Togolese participants to endorse the view that physicians should inform the patient’s family first than among the French. These findings highlight the importance for physicians, at the time of delivering bad news, of considering patients’ cultural values and of tailoring their disclosure approaches to match the diversity of patients’ personal preferences.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".