Systematically reviewing qualitative and quantitative evidence to inform management and policy-making in the health field
Bibliographic record
Abstract
Policy-makers and managers have always used a wide range of sources of evidence in making decisions about policy and the organization of services. However, they are under increasing pressure to adopt a more systematic approach to the utilization of the complex evidence base. Decision-makers must address complicated questions about the nature and significance of the problem to be addressed; the nature of proposed interventions; their differential impact; cost-effectiveness; acceptability and so on. This means that Cochrane-style reviews alone are not sufficient. Rather, they require access to syntheses of high-quality evidence that include research and non-research sources, and both qualitative and quantitative research findings. There is no single, agreed framework for synthesizing such diverse forms of evidence and many of the approaches potentially applicable to such an endeavour were devised for either qualitative or quantitative synthesis and/or for analysing primary data. This paper describes the key stages in reviewing and synthesizing qualitative and quantitative evidence for decision-making and looks at various strategies that could offer a way forward. We identify four basic approaches: narrative (including traditional 'literature reviews' and more methodologically explicit approaches such as 'thematic analysis', 'narrative synthesis', '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). The choice of approach will be contingent on the aim of the review and nature of the available evidence, and often more than one approach will be required.
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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 | 0.578 | 0.776 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.069 | 0.044 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".