Approach to identifying research gaps on vector-borne and other infectious diseases of poverty in urban settings: scoping review protocol from the VERDAS consortium and reflections on the project’s implementation
Bibliographic record
Abstract
BACKGROUND: This paper presents the overall approach undertaken by the "VEctor boRne DiseAses Scoping reviews" (VERDAS) consortium in response to a call issued by the Vectors, Environment and Society unit of the Special Programme for Research and Training in Tropical Diseases hosted by the World Health Organization. The aim of the project was to undertake a broad knowledge synthesis and identify knowledge gaps regarding the control and prevention of vector-borne diseases in urban settings. METHODS: The consortium consists of 14 researchers, 13 research assistants, and one research coordinator from seven different institutions in Canada, Colombia, Brazil, France, Spain, and Burkina Faso. A six-step protocol was developed for the scoping reviews undertaken by the consortium, based on the framework developed by Arksey and O'Malley and improved by Levac et al. In the first step, six topics were identified through an international eDelphi consultation. In the next four steps, the scoping reviews were conducted. The sixth step was the VERDAS workshop held in Colombia in March 2017. DISCUSSION: In this article, we discuss several methodological issues encountered and share our reflections on this work. We believe this protocol provides a strong example of an exhaustive and rigorous process for performing broad knowledge synthesis for any given topic and should be considered for future research initiatives and donor agendas in multiple fields to highlight research needs scientifically.
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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 | 0.498 | 0.385 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".