Systematic reviews of the effectiveness of quality improvement strategies and programmes
Bibliographic record
Abstract
Systematic reviews provide the best evidence on the effectiveness of healthcare interventions including quality improvement strategies. The methods of systematic review of individual patient randomised trials of healthcare interventions are well developed. We discuss methodological and practice issues that need to be considered when undertaking systematic reviews of quality improvement strategies including developing a review protocol, identifying and screening evidence sources, quality assessment and data abstraction, analytical methods, reporting systematic reviews, and appraising systematic reviews. This paper builds on our experiences within the Cochrane Effective Practice and Organisation of Care (EPOC) review group.
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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 | 0.262 | 0.634 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.022 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".