Aligning evidence generation and use across health, development, and environment
Bibliographic record
Abstract
Although health, development, and environment challenges are interconnected, evidence remains fractured across sectors due to methodological and conceptual differences in research and practice. Aligned methods are needed to support Sustainable Development Goal advances and similar agendas. The Bridge Collaborative, an emergent research-practice collaboration, presents principles and recommendations that help harmonize methods for evidence generation and use. Recommendations were generated in the context of designing and evaluating evidence of impact for interventions related to five global challenges (stabilizing the global climate, making food production sustainable, decreasing air pollution and respiratory disease, improving sanitation and water security, and solving hunger and malnutrition) and serve as a starting point for further iteration and testing in a broader set of contexts and disciplines. We adopted six principles and emphasize three methodological recommendations: (1) creation of compatible results chains, (2) consideration of all relevant types of evidence, and (3) evaluation of strength of evidence using a unified rubric. We provide detailed suggestions for how these recommendations can be applied in practice, streamlining efforts to apply multi-objective approaches and/or synthesize evidence in multidisciplinary or transdisciplinary teams. These recommendations advance the necessary process of reconciling existing evidence standards in health, development, and environment, and initiate a common basis for integrated evidence generation and use in research, practice, and policy design.
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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 | 0.811 | 0.858 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.041 | 0.032 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.044 | 0.046 |
| Open science | 0.013 | 0.047 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".