Rapid reviews methods series: guidance on rapid scoping, mapping and evidence and gap map (‘Big Picture Reviews’)
Bibliographic record
Abstract
Scoping, mapping and evidence and gap map reviews ('Big Picture Reviews' (BPRs)) are evidence synthesis methods that address broad research questions.They provide an overview of existing evidence, identify gaps in knowledge and priorities for research.Unlike systematic reviews (SRs) of effectiveness, they do not seek to synthesise findings but to provide a description of the evidence.There has been a growth in the production of rapid BPRs to meet commissioners' and knowledge users' (KUs) needs for timely outputs.No guidance currently exists for the use of rapid approaches in BPRs, and the purpose of this paper is to address this lack.Rapid reviews include simplifying or omitting a variety of methods; however, the approaches may have varying impacts on processes and findings in different types of reviews and should be done with reference to the standard approaches for that particular methodology.BPRs differ from SRs of effectiveness, in terms of their purpose, addressing a broad research question, rather than a specific question which fits a population, intervention, comparator and outcome (PICO) framework.Developing and refining the research question and search strategy may need more time than in a SR.Search yields are typically larger with a greater proportion of time spent on identifying evidence for inclusion when compared with SRs.They do not involve a synthesis of included studies, so the impact of missing data may have less influence on the rigour of the findings than in SRs of the effect of an intervention where a pooled estimate is reported.This paper addresses these differences, and the implications of rapid approaches to BPRs, with recommendations for practice that aim to increase efficiency while maintaining rigour. WHAT IS ALREADY KNOWN ON THIS TOPIC⇒ An increasing number of rapid scoping, mapping reviews and evidence gap maps ('Big Picture Reviews' (BPRs)) are being undertaken to address broad research questions and provide an overview of a topic.While there is guidance on rapid review methods, this has not been tailored to the methods used in scoping, mapping and evidence and gap map (BPRs) reviews. WHAT THIS STUDY ADDS⇒ This paper considers how rapid methods might be applied to BPRs and the implications of these for the rigour and value of the research findings. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY⇒ This is the first paper to provide guidance for the methods of applying rapid approaches to BPRs.It will inform both researchers and users of the potential and limitations of rapid methods in these types of reviews.It highlights gaps in knowledge, including the implications of rapid methods for the trustworthiness of BPR findings and the need for evaluation of technologies that herald opportunities for greater efficiencies in the production of trustworthy evidence syntheses.Protected by copyright, including for uses related to text and data mining, AI training, and similar technologies..
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.216 | 0.448 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.207 | 0.217 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".