Prioritising methodological research questions for scoping reviews, mapping reviews and evidence and gap maps for health research: a protocol for PROSPECT Delphi study
Bibliographic record
Abstract
INTRODUCTION: Scoping reviews, mapping reviews and evidence and gap maps (collectively known as 'big picture reviews') in health continue to gain popularity within the evidence ecosystem. These big-picture reviews are beneficial for policy-makers, guideline developers and researchers within the field of health for understanding the available evidence, characteristics, concepts and research gaps, which are often needed to support the development of policies, guidelines and practice. However, these reviews often face criticism related to poor and inconsistent methodological conduct and reporting. There is a need to understand which areas of these reviews require further methodological clarification and exploration. The aim of this project is to develop a research agenda for scoping reviews, mapping reviews and evidence and gap maps in health by identifying and prioritising specific research questions related to methodological uncertainties. METHODS AND ANALYSIS: A modified e-Delphi process will be adopted. Participants (anticipated N=100) will include patients, clinicians, the public, researchers and others invested in creating a strategic research agenda for these reviews. This Delphi will be completed in four consecutive stages, including a survey collecting the methodological uncertainties for each of the big picture reviews, the development of research questions based on that survey and two further surveys and four workshops to prioritise the research questions. ETHICS AND DISSEMINATION: This study was approved by the University of Adelaide Human Research Ethics Committee (H-2024-188). The results will be communicated through open-access peer-reviewed publications and conferences. Videos and infographics will be developed and placed on the JBI (previously Joanna Briggs Institute) Scoping Review Network webpage.
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.
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 | 0.356 | 0.309 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.053 | 0.019 |
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".