Toward a Consensus on Guiding Principles for Health Systems Strengthening
Bibliographic record
Abstract
A renewed focus on health systems strengthening (HSS) in global health has emerged in recent years. The World Health Organization (WHO) and others have promoted HSS as essential to attaining the Millennium Development Goals and to improving global health outcomes [1],[2]. This recent increase in interest is highlighted by the organization of the First Global Symposium on Health Systems Research, held in November 2010 [3]. Additionally, numerous funding opportunities with an emphasis on HSS have been established, including a collaborative effort between the Global Alliance for Vaccines and Immunization (GAVI Alliance), The Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund), and the World Bank [4], as well as US President Obama's Global Health Initiative [5]. Despite the growing consensus for the need for HSS, there is little agreement on strategies for its implementation [6]. Widely accepted guiding principles could provide a common language for strategy development and communication in the global community. Without a set of agreed-upon principles, frameworks for policy, practice, and evaluation may be unclear, overly narrow, or inconsistent [7], limiting the ability for collective learning, innovation, and improvement. Here we suggest a list of ten guiding principles necessary for effective HSS.
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 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 | 0.220 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.033 | 0.062 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".