Stakeholder engagement opportunities in systematic reviews: Knowledge transfer for policy and practice
Bibliographic record
Abstract
Knowledge transfer and exchange is the process of increasing the awareness and use of research evidence in policy or practice decision making by nonresearch audiences or stakeholders. One way to accomplish this end is through ongoing interaction between researchers and interested nonresearch audiences, which provides an opportunity for the two groups to learn more about one another. The purpose of this article is to describe and discuss various stakeholder engagement opportunities that we employ throughout the stages of conducting a systematic review, to increase knowledge utilization within these audiences. Systematic reviews of the literature on a particular topic can provide an unbiased overview of the state of the literature. The engagement opportunities we have identified are topic consultation, feedback meetings during the review, member of review team, and involvement in dissemination. The potential benefits of including stakeholders in the process of a systematic review include increased relevance, clarity, and awareness of systematic review findings. A further benefit is the potential for increased dissemination of the findings. Challenges that researchers face are that stakeholder interactions can be time- and resource-intensive, it can be difficult balancing stakeholder desires with scientific rigor, and stakeholders may have difficulties accepting findings with which they do not agree. Despite these challenges we have included stakeholder involvement as a permanent step in the procedure of conducting a systematic review.
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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 | 0.812 | 0.856 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.030 | 0.057 |
| Open science | 0.006 | 0.063 |
| Research integrity | 0.023 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".