An Introduction to Systematic Reviews that Include Meta-analyses for Policy Makers and Analysts
Bibliographic record
Abstract
\nMany governments are moving towards evidence-based policy-making (EBPM), but policy-makers and policy analysts may struggle with the vast amounts of research available to them. Systematic reviews and meta-analyses may be useful tools for policy-makers and analysts wishing to implement EBPM. But many policy-makers and analysts may be unfamiliar with the method. This paper provides an introduction to these research methods. It answers the following questions: What are systematic reviews and meta-analyses? How can they be useful to meta-analyses? What are their advantages over other kinds of research? And, what should policy-makers look for when critically evaluating systematic reviews and meta-analyses? The paper concludes with final reflections and resources for policy-makers and analysts wishing to evaluate systematic reviews or conduct their own systematic reviews.\n
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.115 | 0.320 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".