A typology of systematic reviews for synthesising evidence on health care
Bibliographic record
Abstract
OBJECTIVES: The objectives of this paper are to (a) Review published references to systematic reviews; (b) offer a typology of systematic reviews for synthesising evidence on health care; and (c) summarise the guides for designing, reporting and appraising the reviews. BACKGROUND: Systematic reviews play a role in finding, synthesising, transferring and implementing evidence for healthcare policy, practice guidelines and allocation of health resources. They have been particularly successful in confirming or synthesising evidence for health care by meta-analysing aggregated data from multiple randomised controlled trials. However, concerns about the limitations of evidence from controlled trials have prompted interest in other review methods capable of locating and appraising evidence from more diverse, and possibly more realistic, healthcare situations. METHODS: An iterative citation-tracking process with Google Search and grey literature identified 204 papers on previous typologies and methods of systematic reviews. RESULTS AND CONCLUSIONS: There are six types of systematic reviews: narrative; meta-analysis; scoping; qualitative; umbrella; and realist. Each type has distinct objectives, characteristics and attributes, but with much overlapping of methods and guides. Sensitivity to the need for qualitative evidence on complex human responses to ill-health and health care has broadened the objectives and methods of health-related systematic reviews to find, appraise and synthesis useful evidence for practice guidelines, healthcare policy and allocation of health resources.
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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 | 0.250 | 0.384 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.085 | 0.070 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".