Research on vector-borne diseases: implementation of research communication strategies
Bibliographic record
Abstract
BACKGROUND: Effective communication of research findings on vector-borne diseases in Africa is challenging for a number of reasons. Following the experiences of a number of researchers over the life of a project, this article looks for lessons that can be shared with the wider research community. MAIN BODY: Between 2014 and 2017, a set of five inter-disciplinary teams from seven African countries collaborated on a project focusing on vector-borne diseases in the context of climate change. A central objective of this work was to influence policy and programming with relevant research findings. This article examines how principles of research communication, derived from the literature and current guidelines, can be applied in practice. Several challenges and lessons are highlighted, showing that research communication takes place within difficult constraints and in complex, fluid institutional and political environments. The processes of communication between policymakers and researchers including stakeholder mapping, defining research communication plans and tailoring communication products are discussed. CONCLUSIONS: The article concludes that while guidelines and frameworks for research communication are helpful, they should not detract from the ability of local teams to adapt to circumstances. Of key importance are the relationships and networks of local research teams.
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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 | 0.459 | 0.436 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".