Paper 3: Selecting rapid review methods for complex questions related to health policy and system issues
Bibliographic record
Abstract
Approaches for rapid reviews that focus on streamlining systematic review methods are not always suitable for exploring complex policy questions, as developing and testing theories to explain these complexities requires configuring diverse qualitative, quantitative, and mixed methods studies. Our objective was therefore to provide a guide to selecting approaches for rapidly (i.e., within days to months) addressing complex questions related to health policy and system issues.We provide a two-stage, transdisciplinary collaborative process to select a rapid review approach to address complex policy questions, which consists of scoping the breadth and depth of the literature and then selecting an optimal approach to synthesis. The first stage (scoping the literature) begins with a discussion with the stakeholders requesting evidence to identify and refine the question for the review, which is then used to conduct preliminary searches and conceptually map the documents identified. In the second stage (selection of an optimal approach), further stakeholder consultation is required to refine and tailor the question and approach to identifying relevant documents to include. The approach to synthesizing the included documents is then guided by the final question, the breadth and depth of the literature, and the time available and can include a static or evolving conceptual framework to code and analyze a range of evidence. For areas already covered extensively by existing systematic reviews, the focus can be on summarizing and integrating the review findings, resynthesizing the primary studies, or updating the search and reanalyzing one or more of the systematic reviews.The choice of approaches for conducting rapid reviews is intertwined with decisions about how to manage projects, the amount of work to be done, and the knowledge already available, and our guide offers support to help make these strategic decisions.
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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 | 0.616 | 0.784 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.017 |
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".