Evidence synthesis, economics and public policy
Bibliographic record
Abstract
Systematic reviews and syntheses of evidence are increasingly used to inform public policy decisions. Growing budgetary pressures mean that decision makers often need to consider evidence on the costs and efficiency of alternatives as well as their effects. There are a number of methodological challenges in the identification, appraisal, synthesis, interpretation and use of economic evidence. This article draws on a recently published edited volume to review the latest developments, proposals and controversies in these aspects of economic evidence synthesis methodology. It focuses on two broad classes of approach: systematic review to summarize and compare the findings of existing economic analyses and synthesis of new economic results using decision models. The availability and scope of economic evidence is currently limited in many fields, but improving. Increased engagement between economists, the wider evidence synthesis community, and decision makers is needed to improve both the production and use of economic evidence. Further research to improve the evidence base that underpins application of economic evidence synthesis methodology will need to embrace a broader range of methods than economic evaluation and systematic review alone. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.238 | 0.548 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.004 |
| Bibliometrics | 0.033 | 0.033 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".