Knowledge Translation of Economic Evaluations and Network Meta-Analyses
Bibliographic record
Abstract
Economic evaluations and network meta-analyses (NMAs) are complex methodologies. Increasing their transparency and accessibility could enhance confidence in the legitimacy of policy decisions informed by these analyses. Four systematic reviews were conducted to understand policymakers’ informational needs and to determine what guidance researchers have on how to present economic evaluations and NMAs. Qualitative interviews were conducted with Canadian policymakers, i.e., knowledge users, to understand barriers and facilitators to using and communicating economic evaluations and NMAs and with individuals in international health technology assessment organizations, i.e. knowledge producers, to explore current approaches to communicating economic evaluations and NMAs. A toolkit for NMAs and economic evaluations was proposed based on an integrated review of these findings and guided by the Knowledge-to-Action framework. Examples of tools were developed and applied to an economic evaluation and NMA of osteoporosis therapies. Systematic reviews and qualitative interviews found that communication approaches that provide robust content, identify contextual factors relevant to policy decisions and enhance clarity were valued. Twelve tools were proposed that enhance communication, education and access to resources for policymakers. Two of these tools were developed: Economic Guidance for Researchers and NMA Guidance for Researchers.
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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 | 0.371 | 0.812 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 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".