The Malawi NCD BRITE Consortium: Building Research Capacity, Implementation, and Translation Expertise for Noncommunicable Diseases
Bibliographic record
Abstract
Africa is experiencing an increasing prevalence of noncommunicable diseases (NCD). However, few reliable data are available on their true burden, main risk factors, and economic impact that are needed to inform implementation of evidence-based interventions in the local context. In Malawi, a number of initiatives have begun addressing the NCD challenge, which have often utilized existing infectious disease infrastructure. It will be crucial to carefully leverage these synergies to maximize their impact. NCD-BRITE (Building Research Capacity, Implementation, and Translation Expertise) is a transdisciplinary consortium that brings together key research institutions, the Ministry of Health, and other stakeholders to build long-term, sustainable, NCD-focused implementation research capacity. Led by University of Malawi-College of Medicine, University of North Carolina, and Dignitas International, NCD-BRITE's specific aims are to conduct detailed assessments of the burden and risk factors of common NCD; assess the research infrastructure needed to inform, implement, and evaluate NCD interventions; create a national implementation research agenda for priority NCD; and develop NCD-focused implementation research capacity through short courses, mentored research awards, and an internship placement program. The capacity-building activities are purposely designed around the University of Malawi-College of Medicine and Ministry of Health to ensure sustainability. The NCD BRITE Consortium was launched in February 2018. In year 1, we have developed NCD-focused implementation research capacity. Needs assessments will follow in years 2 and 3. Finally, in year 4, the generated research capacity, together with findings from the needs assessments, will be used to create a national, actionable, implementation research agenda for NCD prioritized in this consortium, namely cardiovascular disease, diabetes mellitus, and asthma and chronic obstructive pulmonary disease.
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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 | 0.137 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.006 | 0.050 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".