Stakeholder engagement opportunities in systematic reviews: Knowledge transfer for policy and practice
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
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.
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.
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 | 0.044 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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