How can we synthesise qualitative and quantitative evidence for policy makers and managers?
Bibliographic record
Abstract
Objectives: To describe how different types of evidence - qualitative, quantitative and non-research based - can be integrated/synthesised to inform policy decision making.<br/>Study design: Review and critical commentary on methods for synthesis used in health and social science research, undertaken in 2004.<br/>Principle findings: We identify four basic approaches to reviewing and synthesising evidence that have potential to inform policy decision making.: narrative (including traditional ‘literature reviews’ and more methodologically explicit approaches such as narrative synthesis, thematic analysis, ‘realist synthesis’ and ‘meta-narrative mapping’), qualitative (which convert all available evidence into qualitative form using techniques such as ‘meta-ethnography’ and ‘qualitative cross-case analysis’), quantitative (which convert all evidence into quantitative form using techniques such as ‘quantitative case survey’ or ‘content analysis’) and Bayesian meta-analysis and decision analysis (which can convert qualitative evidence such as preferences about different outcomes into quantitative form or ‘weights’ to use in quantitative synthesis).<br/>Conclusion: There is no single, agreed framework for synthesising diverse forms of evidence. Many of the methods that show potential for this have been devised for reviews which include either qualitative or quantitative evidence rather than those that attempt tointegrate/synthesis both types of evidence. Methods for synthesis are evolving – some are less well developed than others. Nonetheless we must learn to synthesise diverse forms of evidence if we are to better meet the needs of policy makers.<br/>Implications: Policy makers have always used a widerange of sources of evidence in making decisions about policy and service organisation but are under pressureto adopt a more systematic approach to the utilisationof this complex evidence base. Synthesis is an attractive solution. The choice of approach is contingent on the policy questions and the nature ofthe evidence. More policy-research dialogue is required to develop synthesis methods.<br/>Primary funding: Canadian HSR Foundation &amp; NHS R&amp;DSDO
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".