Scoping reviews and their role in identifying research priorities
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Scoping reviews have been identified as appropriate methodologies to contribute to our knowledge. The objective of this review is to summarize how scoping reviews can be used to identify research priorities. METHODS: Based on our experience as evidence synthesis methodologists and researchers, the Joanna Briggs Institute (JBI) scoping review methodology group, have identified the potential roles of scoping reviews in identification of research priorities. RESULTS: Scoping reviews typically ask broad questions that allow researchers to obtain an overview or map of the existing evidence. Scoping reviews also incorporate multiple levels of evidence that enriches the strength of the knowledge that is gained. This value is revealed by the use of scoping reviews to contribute to and perform the following functions: 1) map a research area and identify gaps that need to be addressed; 2) prioritize research topics by identifying key issues to investigate; 3) identify the type of study designs that have been used to investigate a particular topic, and/or the range of outcomes measured following a specific intervention; 4) identify the essential contextual factors that are relevant to the study of a particular research topic; 5) identify equity issues in the research field; 6) assist in engaging stakeholders and/or experts in the field by facilitating the inclusion of these stakeholders within the research process; and 7) provide the relevant new knowledge to enhance and support applications for funding. CONCLUSION: To ensure this contribution to identifying research priorities is reliable, scoping reviews must be performed following the existing rigorous methodological processes and adhere to the currently available reporting guidelines. By doing so, scoping reviews have great potential to identify research priorities, to guide the expansion of research and the generation of new knowledge.
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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 | 0.751 | 0.857 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.016 | 0.008 |
| Bibliometrics | 0.073 | 0.072 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.066 | 0.054 |
| Open science | 0.013 | 0.040 |
| Research integrity | 0.023 | 0.028 |
| Insufficient payload (model declined to judge) | 0.015 | 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".