AHRQ series on complex intervention systematic reviews—paper 3: adapting frameworks to develop protocols
Bibliographic record
Abstract
BACKGROUND: Once a proposed topic has been identified for a systematic review and has undergone a question formulation stage, a protocol must be developed that specifies the scope and research questions in detail and outlines the methodology for conducting the systematic review. RATIONALE: Framework modifications are often needed to accommodate increased complexity. We describe and give examples of adaptations and alternatives to traditional analytic frameworks. DISCUSSION: This article identifies and describes elements of frameworks and how they can be adapted to inform the protocol and conduct of systematic reviews of complex interventions. Modifications may be needed to adapt the population, intervention, comparators, and outcomes normally used in protocol development to successfully describe complex interventions; in some instances, alternative frameworks may be better suited. Possible approaches to analytic frameworks for complex interventions that illustrate causal and associative linkages are outlined, including time elements, which systematic reviews of complex interventions may need to address. The need for and specifics of the accommodations vary with details of a specific systematic review. This in turn helps determine whether traditional frameworks are sufficient, can be refined, or if alternate frameworks must be adopted.
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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 | 0.689 | 0.826 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.012 | 0.025 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".