Palliative Care Development in Africa: Lessons From Uganda and Kenya
Bibliographic record
Abstract
PURPOSE: Despite increased access to palliative care in Africa, there remains substantial unmet need. We examined the impact of approaches to promoting the development of palliative care in two African countries, Uganda and Kenya, and considered how these and other strategies could be applied more broadly. METHODS: This study reviews published data on development approaches to palliative care in Uganda and Kenya across five domains: education and training, access to opioids, public and professional attitudes, integration into national health systems, and research. These countries were chosen because they are African leaders in palliative care, in which successful approaches to palliative care development have been used. RESULTS: Both countries have implemented strategies across all five domains to develop palliative care. In both countries, successes in these endeavors seem to be related to efforts to integrate palliative care into the national health system and educational curricula, the training of health care providers in opioid treatment, and the inclusion of community providers in palliative care planning and implementation. Research in palliative care is the least well-developed domain in both countries. CONCLUSION: A multidimensional approach to development of palliative care across all domains, with concerted action at the policy, provider, and community level, can improve access to palliative care in African countries.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".