Rapid reviews methods series: guidance on rapid scoping, mapping and evidence and gap map (‘Big Picture Reviews’)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it