Breaking Bad News to Togolese Patients
Bibliographic record
Abstract
The aim of this study was to map Togolese people's positions regarding the breaking of bad news to elderly patients. Two hundred eleven participants 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 acceptability of the physician's conduct in each case. The vignettes were all combinations of five factors: (a) the severity of the disease, (b) the patient's wishes about disclosure, (c) the level of social support during hospitalization, (d) the patient's psychological robustness, and (e) the physician's decision about how to communicate the bad news. Five qualitatively different positions were found. Two percent of the participants preferred that the physician always tell the full truth to both the patient and her relatives, 8% preferred that the truth be told depending on the physician's perception of the situation, 15% preferred that the physician tell the truth but understood that in some cases nondisclosure to the patient was not inappropriate, 33% preferred that the physician tell the full truth to the relatives but not as much information to the patient, and 42% preferred that the physician tell the full truth to the relatives only. These findings present a challenge to European physicians taking care of African patients living in Europe or working in African hospitals, and to African physicians trained in Europe and now working in their home countries. If these physicians respect the imperative of always telling the truth directly to their patients, their behavior may trigger anger and considerable misunderstanding among African patients and their families.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".