Moving from consultation to co-creation with knowledge users in scoping reviews: guidance from the JBI Scoping Review Methodology Group
Bibliographic record
Abstract
ABSTRACT: Knowledge user consultation is often limited or omitted in the conduct of scoping reviews. Not including knowledge users within the conduct and reporting of scoping reviews could be due to a lack of guidance or understanding about what consultation requires and the subsequent benefits. Knowledge user engagement in evidence synthesis, including consultation approaches, has many associated benefits, including improved relevance of the research and better dissemination and implementation of research findings. Scoping reviews, however, have not been specifically focused on in terms of research into knowledge user consultation and evidence syntheses. In this paper, we will present JBI's guidance for knowledge user engagement in scoping reviews based on the expert opinion of the JBI Scoping Review Methodology Group. We offer specific guidance on how this can occur and provide information regarding how to report and evaluate knowledge user engagement within scoping reviews. We believe that scoping review authors should embed knowledge user engagement into all scoping reviews and strive towards a co-creation model.
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 | 0.613 | 0.706 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.011 | 0.036 |
| Research integrity | 0.027 | 0.027 |
| Insufficient payload (model declined to judge) | 0.014 | 0.025 |
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".