A time-responsive tool for informing policy making: rapid realist review
Bibliographic record
Abstract
BACKGROUND: A realist synthesis attempts to provide policy makers with a transferable theory that suggests a certain program is more or less likely to work in certain respects, for particular subjects, in specific kinds of situations. Yet realist reviews can require considerable and sustained investment over time, which does not always suit the time-sensitive demands of many policy decisions. 'Rapid Realist Review' methodology (RRR) has been developed as a tool for applying a realist approach to a knowledge synthesis process in order to produce a product that is useful to policy makers in responding to time-sensitive and/or emerging issues, while preserving the core elements of realist methodology. METHODS: Using examples from completed RRRs, we describe key features of the RRR methodology, the resources required, and the strengths and limitations of the process. All aspects of an RRR are guided by both a local reference group, and a group of content experts. Involvement of knowledge users and external experts ensures both the usability of the review products, as well as their links to current practice. RESULTS: RRRs have proven useful in providing evidence for and making explicit what is known on a given topic, as well as articulating where knowledge gaps may exist. From the RRRs completed to date, findings broadly adhere to four (often overlapping) classifications: guiding rules for policy-making; knowledge quantification (i.e., the amount of literature available that identifies context, mechanisms, and outcomes for a given topic); understanding tensions/paradoxes in the evidence base; and, reinforcing or refuting beliefs and decisions taken. CONCLUSIONS: 'Traditional' realist reviews and RRRs have some key differences, which allow policy makers to apply each type of methodology strategically to maximize its utility within a particular local constellation of history, goals, resources, politics and environment. In particular, the RRR methodology is explicitly designed to engage knowledge users and review stakeholders to define the research questions, and to streamline the review process. In addition, results are presented with a focus on context-specific explanations for what works within a particular set of parameters rather than producing explanations that are potentially transferrable across contexts and populations. For policy makers faced with making difficult decisions in short time frames for which there is sufficient (if limited) published/research and practice-based evidence available, RRR provides a practical, outcomes-focused knowledge synthesis method.
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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 | 0.531 | 0.734 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.046 | 0.026 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".