International Partnerships to Develop Evidence-informed Priority Setting Institutions: Ten Years of Experience from the International Decision Support Initiative (iDSI)
Bibliographic record
Abstract
All health systems must set priorities. Evidence-informed priority-setting (EIPS) is a specific form of systematic priority-setting which involves explicit consideration of evidence to determine the healthcare interventions to be provided. The international Decision Support Initiative (iDSI) was established in 2013 as a collaborative platform to catalyze faster progress on EIPS, particularly in low- and middle-income countries. This article summarizes the successes, challenges, and lessons learned from ten years of iDSI partnering with countries to develop EIPS institutions and processes. This is a thematic documentary analysis, structured by iDSI's theory of change, extracting successes, challenges, and lessons from three external evaluations and 19 internal reports to funders. We identified three phases of iDSI's work-inception (2013-15), scale-up (2016-2019), and focus on Africa (2019-2023). iDSI has established a global platform for coordinating EIPS, advanced the field, and supported regional networks in Asia and Africa. It has facilitated progress in securing high-level commitment to EIPS, strengthened EIPS institutions, and developed capacity for health technology assessments. This has resulted in improved decisions on service provision, procurement, and clinical care. Major lessons learned include the importance of sustained political will to develop EIPS; a clear EIPS mandate; inclusive governance structures appropriate to health financing context; politically sensitive and country-led support to EIPS, taking advantage of policy windows for EIPS reforms; regional networks for peer support and long-term sustainability; utilization of context appropriate methods such as adaptive HTA; and crucially, donor-funded global health initiatives supporting and integrating with national EIPS systems, not undermining them.
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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 | 0.163 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".