Strengthening the health workforce to support integration of HIV and noncommunicable disease services in sub-Saharan Africa
Bibliographic record
Abstract
OBJECTIVE: The successful expansion of HIV services in sub-Saharan Africa has been a signature achievement of global public health. This article explores health workforce-related lessons from HIV scale-up, their implications for integrating noncommunicable disease (NCD) services into HIV programs, ways to ensure that healthcare workers have the knowledge, skills, resources, and enabling environment they need to provide comprehensive integrated HIV/NCD services, and discussion of a priority research agenda. DESIGN AND METHODS: We conducted a scoping review of the published and 'gray' literature and drew upon our cumulative experience designing, implementing and evaluating HIV and NCD programs in low-resource settings. RESULTS AND CONCLUSION: Lessons learned from HIV programs include the role of task shifting and the optimal use of multidisciplinary teams. A responsible and adaptable policy environment is also imperative; norms and regulations must keep pace with the growing evidence base for task sharing, and early engagement of regulatory authorities will be needed for successful HIV/NCD integration. Ex-ante consideration of work culture will also be vital, given its impact on the quality of service delivery. Finally, capacity building of a robust interdisciplinary workforce is essential to foster integrated patient-centered care. To succeed, close collaboration between the health and higher education sectors is needed and comprehensive competency-based capacity building plans for various health worker cadres along the education and training continuum are required. We also outline research priorities for HIV/NCD integration in three key domains: governance and policy; education, training, and management; and service delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".