Establishing research priorities in prevention and control of vector-borne diseases in urban areas: a collaborative process
Bibliographic record
Abstract
BACKGROUND: In 2015, following a call for proposals from the Special Programme for Research and Training in Tropical Diseases (TDR), six scoping reviews on the prevention and control of vector-borne diseases in urban areas were conducted. Those reviews provided a clear picture of the available knowledge and highlighted knowledge gaps, as well as needs and opportunities for future research. Based on the research findings of the scoping reviews, a concept mapping exercise was undertaken to produce a list of priority research needs to be addressed. METHODS: Members of the six research teams responsible for the "VEctor boRne DiseAses Scoping reviews" (VERDAS) consortium's scoping reviews met for 2 days with decision-makers from Colombia, Brazil, Peru, Pan-American Health Organization, and World Health Organization. A total of 11 researchers and seven decision-makers (from ministries of health, city and regional vector control departments, and vector control programs) completed the concept mapping, answering the question: "In view of the knowledge synthesis and your own expertise, what do we still need to know about vector-borne diseases and other infectious diseases of poverty in urban areas?" Participants rated each statement on two scales from 1 to 5, one relative to 'priority' and the other to 'policy relevance', and grouped statements into clusters based on their own individual criteria and expertise. RESULTS: The final map consisted of 12 clusters. Participants considered those entitled "Equity", "Technology", and "Surveillance" to have the highest priority. The cluster considered the most important concerns equity issues, confirming that these issues are rarely addressed in research on vector-borne diseases. On the other hand, the "Population mobility" and "Collaboration" clusters were considered to be the lowest priority but remained identified by participants as research priorities. The average policy relevance scores for each of the 12 clusters were roughly the same as the priority scores for all clusters. Some issues were not addressed during the brain-storming. This is the case for governance and for access and quality of care. CONCLUSIONS: Based on this work, and adopting a participatory approach, the concept mapping exercise conducted collaboratively with researchers from these teams and high-level decision-makers identified research themes for which studies should be carried out as a priority.
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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 | 0.455 | 0.387 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.010 | 0.043 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".