Psychosocial and palliative care in African national cancer control plans: A qualitative study
Bibliographic record
Abstract
OBJECTIVE: Low and middle income countries of Africa account for a disproportionate amount of the global health burden of cancer. Providing patients access to psychosocial oncology and palliative care through policy structures such as the National Cancer Control Plans (NCCP) is essential to improving the care provided to patients and their families. The first phase of this study sought to determine the extent to which palliative care and psychosocial oncology were integrated in NCCPs in African countries. METHODS: A qualitative thematic analysis of the plans was used using Nvivo, with two-raters coding and continuous team discussions. Data were organized into an infographic map showing the coverage of themes across African countries. RESULTS: Fifty-eight NCCPs and NCD plans were analyzed in the 54 countries in Africa. The findings illustrate a lack of standardization across countries' NCCPs in addressing psychosocial oncology and palliative care themes. Certain areas presented good coverage across several plans, such as barriers to access, education, awareness, and health behaviors, coordination of care, families, caregivers and community involvement, and palliative care. Other themes presented low coverage, such as doctor-patient communication, mental health, bereavement, psychosocial care, survivorship care, and traditional medicine. CONCLUSIONS: One may consider further developing NCCP areas as they pertain to psychosocial oncology and palliative care to ensure their proper place on the policy agenda for a healthier Africa.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".