Barriers to communicating a cancer diagnosis to patients in a low- to middle-income context
Bibliographic record
Abstract
Abstract Objective. The aim of this study was to understand the needs and experiences of oncology professionals involved in communicating a diagnosis to adult and pediatric cancer patients in the low-middle income (LMI) context of Kenya, with a focus on identifying barriers and facilitators. Methods. A World Café focus group methodology was conducted and comprised 19 discussion groups of approximately 6 participants (n = 114 professionals). Thematic analysis was used to clarify barriers and facilitators of professional–patient communication. Results. Participants reported several obstacles that hinder communication between cancer patients and healthcare professionals in Kenya, including: patient-related barriers (ie, lack of terminology, health literacy), culture-related barriers (ie, cultural and religious beliefs about cancer, beliefs about children), and physician- and system-related barriers (ie, limited communication skills, organizational barriers). Communication facilitators included: the central role of family and cultural traditions, and the caring attitude of physicians when disclosing diagnosis and treatment procedures. Conclusions. The data indicate the potential need to adapt communication guidelines to the Kenyan context to disseminate skills training in a culturally relevant way. Also of importance may be to embed parallel public health strategies and health care structural changes to facilitate disclosure and mitigate any unintended negative effects such as stigma and social isolation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".