Strengthening health systems in low-income countries by enhancing organizational capacities and improving institutions
Bibliographic record
Abstract
BACKGROUND: This paper argues that the global health agenda tends to privilege short-term global interests at the expense of long-term capacity building within national and community health systems. The Health Systems Strengthening (HSS) movement needs to focus on developing the capacity of local organizations and the institutions that influence how such organizations interact with local and international stakeholders. DISCUSSION: While institutions can enable organizations, they too often apply requirements to follow paths that can stifle learning and development. Global health actors have recognized the importance of supporting local organizations in HSS activities. However, this recognition has yet to translate adequately into actual policies to influence funding and practice. While there is not a single approach to HSS that can be uniformly applied to all contexts, several messages emerge from the experience of successful health systems presented in this paper using case studies through a complex adaptive systems lens. Two key messages deserve special attention: the need for donors and recipient organizations to work as equal partners, and the need for strong and diffuse leadership in low-income countries. An increasingly dynamic and interdependent post-Millennium Development Goals (post-MDG) world requires new ways of working to improve global health, underpinned by a complex adaptive systems lens and approaches that build local organizational capacity.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".