Serious Illness Communication in Cancer Care in Africa: A Scoping Review of Empirical Research
Bibliographic record
Abstract
PURPOSE Serious illness communication (SIC) in cancer care describes conversations between clinicians, patients, and families about prognosis and treatment decisions. Cultural context influences SIC. Researchers have studied SIC across diverse settings in Africa. We aimed to describe and synthesize the heterogeneous body of research on SIC practices, preferences, and needs in Africa to identify research and training priorities. METHODS Our search strategy identified studies that focused on SIC within cancer or palliative care in Africa. Following PRISMA guidelines, a systematic literature search was performed using PubMed, Embase, Web of Science, CINAHL, African Index Medicus, and PsycINFO, yielding 1811 unique titles. After sequential review of abstracts, full text, and cited references, 42 articles met inclusion criteria. Quantitative and qualitative data describing study characteristics, aims, methods, and findings were abstracted and analyzed using descriptive statistics and thematic analysis. Critical appraisal was performed using the Mixed Methods Appraisal Tool. RESULTS The 42 included articles were published from 1997-2021, half since 2017, representing 16 countries and all African Union regions: West (33%), East (29%), South (21%), North (12%), and Central (5%). Most study designs were qualitative (45%) or quantitative surveys (50%). Study participants included patients (35%), family caregivers (18%), doctors (18%), nurses (12%), and/or other (11%). Study aims focused on disclosure of diagnosis (27%) or prognosis (20%), breaking bad news (15%), general patient-clinician communication (12%), truth-telling (8%), shared decision-making (7%), information needs/preferences (5%), and/or advance care planning (5%). Despite diverse contexts, common themes emerged. Study authors frequently recommended communication skills training. Critical appraisal demonstrated high quality of studies overall. CONCLUSION Research on SIC in Africa has increased in recent years. Most studies have focused on information delivery by clinicians; fewer on eliciting information from patients (eg, shared decision-making, advanced care planning). Significant opportunities exist for further study and for communication skills training.
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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 | 0.021 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".