Communicating bad news to patients and families in African oncology settings
Bibliographic record
Abstract
AIMS: To assess clinicians' self-reported knowledge of current policies in African oncology settings, of their personal communication practices around sharing bad news with patients, and to identify barriers to the sharing of serious news in these settings. METHODS: A cross-sectional study of cancer care providers in African oncology settings (N = 125) was conducted. Factor analysis was used to assess cross-cultural adaptation and uptake of an evidence-based protocol for disclosing bad news to patients with cancer and of providers' perceived barriers to disclosing bad news to patients with cancer. Analysis of Various (ANOVA) was used to assess strength of association with each dimension of these two measurement models by various categorical variables. RESULTS: Providers from Nigeria, Kenya, Ghana, and Rwanda represented 85% of survey respondents. Two independent, psychometrically reliable, multi-dimensional measurement models were derived to assess providers' personal communication practices and providers' perceived barriers to disclosing a cancer diagnosis. Forty percent (40%) of respondent nurses but only 20% of respondent physicians had had formal communications skills training. Approximately 20%-25% of respondent physicians and nurses reported having a consistent plan or strategy for communicating bad news to their cancer patients. CONCLUSIONS: Results show that effective communication about cancer diagnosis and prognosis requires an appreciation and clinical skill set that blends an understanding of cancer-related internalized stigmas harbored by patient and family, dilemmas posed by treatment affordability, and the need to navigate family wishes about cancer-related diagnoses in the context of African oncology settings. Findings underscore the need for culturally grounded communications research and program design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".