Recommendations for Intersectoral Collaboration for the Prevention and Control of Vector-Borne Diseases: Results From a Modified Delphi Process
Bibliographic record
Abstract
BACKGROUND: Intersectoral collaboration in the context of the prevention and control of vector-borne diseases has been broadly described in both the literature and the current global strategy by the World Health Organization. Our aim was to develop a framework that will distill the currently known multiple models of collaboration. METHODS: Qualitative content analysis and logic modeling of data abstracted from 69 studies included in a scoping review done by the authors were used to develop 9 recommendation statements that summarized the composition and attributes of multisectoral approaches, which were then subjected to a modified Delphi process with 6 experts in the fields of health policy and infectious diseases. RESULTS: Consensus for all statements was achieved during the first round. The recommendation statements were on (1-3) sectoral engagement to supplement government efforts and augment public financing; (4) development of interventions for most systems levels; (5-6) investment in human resource, including training; (7-8) intersectoral action to implement strategies and ensure sustainability of initiatives; and (9) research to support prevention and control efforts. CONCLUSIONS: The core of intersectoral action to prevent vector-borne diseases is collaboration among multiple stakeholders to develop, implement, and evaluate initiatives at multiple levels of intervention.
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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 | 0.310 | 0.271 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".