Sector wide approaches for health in small island states: Lessons learned from the Solomon Islands
Bibliographic record
Abstract
Sector Wide Approaches (SWAps) have increasingly been implemented in countries around the world as a mechanism for effective delivery of health sector funding from various sources. Despite the global focus on aid effectiveness, SWAps have been under-examined. In 2007, the Solomon Islands and development partners began discussing a health SWAp making the Solomon Islands one of the first fragile states globally to adopt a SWAp. This paper explores the establishment and implementation of a health SWAp in the Solomon Islands as a specific case study with lessons learned for the region as well as for aid architecture in fragile states more generally. Tensions between donors and the government impeded agreement and early implementation and country ownership of the SWAp idea was muted. Since mid-2009, however, the Solomon Islands SWAp has made strong progress with greater government ownership and with more focus on partnership and harmonisation rather than on funding mechanisms. The SWAp mechanism has been a challenge for the capacity-constrained Solomon Islands health sector and for development partners familiar with other aid modalities, but current momentum suggests that the SWAp will have a positive impact on adherence to agreed aid effectiveness principles.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".