Building a knowledge translation platform in Malawi to support evidence-informed health policy
Bibliographic record
Abstract
With the support of the World Health Organization's Evidence-Informed Policy Network, knowledge translation platforms have been developed throughout Africa, the Americas, Eastern Europe, and Asia to further evidence-informed national health policy. In this commentary, we discuss the approaches, activities and early lessons learned from the development of a Knowledge Translation Platform in Malawi (KTPMalawi). Through ongoing leadership, as well as financial and administrative support, the Malawi Ministry of Health has strongly signalled its intention to utilize a knowledge translation platform methodology to support evidence-informed national health policy. A unique partnership between Dignitas International, a medical and research non-governmental organization, and the Malawi Ministry of Health, has established KTPMalawi to engage national-level policymakers, researchers and implementers in a coordinated approach to the generation and utilization of health-sector research. Utilizing a methodology developed and tested by knowledge translation platforms across Africa, a stakeholder mapping exercise and initial capacity building workshops were undertaken and a multidisciplinary Steering Committee was formed. This Steering Committee prioritized the development of two initial Communities of Practice to (1) improve data utilization in the pharmaceutical supply chain and (2) improve the screening and treatment of hypertension within HIV-infected populations. Each Community of Practice's mandate is to gather and synthesize the best available global and local evidence and produce evidence briefs for policy that have been used as the primary input into structured deliberative dialogues. While a lack of sustained initial funding slowed its early development, KTPMalawi has greatly benefited from extensive technical support and mentorship by an existing network of global knowledge translation platforms. With the continued support of the Malawi Ministry of Health and the Evidence-Informed Policy Network, KTPMalawi can continue to build on its role in facilitating the use of evidence in the development and refinement of health policy in Malawi.
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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 | 0.148 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".