Building a knowledge translation platform in Malawi to support evidence-informed health policy
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
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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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | MetaresearchScholarly communication Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
| opus | MetaresearchScholarly communication Domain: Reporting · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.062 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it