Evidence-informed capacity building for setting health priorities in low- and middle-income countries: A framework and recommendations for further research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Priority-setting in health is risky and challenging, particularly in resource-constrained settings. It is not simply a narrow technical exercise, and involves the mobilisation of a wide range of capacities among stakeholders - not only the technical capacity to "do" research in economic evaluations. Using the Individuals, Nodes, Networks and Environment (INNE) framework, we identify those stakeholders, whose capacity needs will vary along the evidence-to-policy continuum. Policymakers and healthcare managers require the capacity to commission and use relevant evidence (including evidence of clinical and cost-effectiveness, and of social values); academics need to understand and respond to decision-makers' needs to produce relevant research. The health system at all levels will need institutional capacity building to incentivise routine generation and use of evidence. Knowledge brokers, including priority-setting agencies (such as England's National Institute for Health and Care Excellence, and Health Interventions and Technology Assessment Program, Thailand) and the media can play an important role in facilitating engagement and knowledge transfer between the various actors. Especially at the outset but at every step, it is critical that patients and the public understand that trade-offs are inherent in priority-setting, and careful efforts should be made to engage them, and to hear their views throughout the process. There is thus no single approach to capacity building; rather a spectrum of activities that recognises the roles and skills of all stakeholders. A range of methods, including formal and informal training, networking and engagement, and support through collaboration on projects, should be flexibly employed (and tailored to specific needs of each country) to support institutionalisation of evidence-informed priority-setting. Finally, capacity building should be a two-way process; those who build capacity should also attend to their own capacity development in order to sustain and improve impact.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.101 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it