Understanding the role and organization of health workers delivering non-communicable disease management in primary care in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: Non-communicable diseases are responsible for three-fourths of annual deaths worldwide and disproportionately affect individuals living in low- and middle-income countries. As populations age and the burden of chronic diseases rises, the role of the health workforce becomes increasingly vital in ensuring equitable access to prevention, early detection, and appropriate treatment. This study aims to review and synthesize the existing knowledge on the role and organization of health workers in managing non-communicable diseases in primary care in low- and middle-income countries. METHODS: We followed the PRISMA-SCR guidelines and conducted a scoping review in the MEDLINE, EMBASE, CINAHL, and Global Index Medicus databases. We included studies that addressed a non-communicable disease, specified the health workers involved, and reported on models of care for chronic disease management. These studies were published in English. RESULTS: We identified 175 articles. One hundred twenty-five (71%) highlighted the role of multidisciplinary teams, and 41 (23%) discussed optimizing roles within teams for the management of non-communicable diseases in primary care. Multidisciplinary teams often included generalist physicians, nurses, and community health workers. Optimizing roles within teams involves redistributing tasks from doctors to nurses or community health workers and from nurses to community health workers. CONCLUSIONS: Multidisciplinary teams and optimizing health workers' roles within teams are important in delivering non-communicable disease management. Multidisciplinary teams typically included generalist physicians, nurses, and community health workers, while additional non-physician health workers depended on the most prevalent health conditions being addressed in the community served. Optimizing the roles of health workers provides opportunities to redistribute responsibilities to deliver more comprehensive care.
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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 | 0.020 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".