Role and Level of Engagement of Peer Researchers in Systematic Reviews: A Review Article
Bibliographic record
Abstract
Aims: This article aims at examining the role of peer researchers in systematic reviews of sensitive topics. While there are articles on the participation of peer researchers in primary research, there are very few studies on their role in systematic reviews. This project asks three research questions: What role do refugee/immigrant peers play in the systematic literature reviews of collaborative research models? What are the effective models used for engaging peer researchers in conducting systematic reviews of literature? In what ways are peer researchers being used in systematic reviews?.
 Study Design and Methodology: this is a review article. Ovid Medline, Embase, Scopus, CINAHL, and Web of Science databases were consulted to understand the extent, knowledge gap, and scope of this systematic literature review. The team developed Boolean operators with four keywords: (i) systematic review, (ii) role or contribution or participation or engagement, (iii) peer research or collaborative research, and (iv) participatory research. In total, 270 articles were found, from which 99 were duplicates, 164 articles were removed after checking their title and abstract, and seven articles were selected for full article review.
 Results: All seven articles were systematic reviews focusing on the involvement of peer researchers in the healthcare field and described succinctly the role of peer researchers in conducting systematic reviews. Of them, two articles described peers’ involvement during the systematic review's design, methodology, and analysis activities; however, the involvement of immigrants/refugees as peer researchers in systematic reviews was not available despite repeated intentional searches. There is no mention of engaging any refugee peers in research on refugee interest.
 Conclusion: Some studies show the benefits of involving peer researchers in a collaborative design. However, there is a scope for generating more evidence regarding the roles of refugee/immigrant peer-researchers in systematic reviews. From our practice, we recommend engaging at least two peers or 20% of the members of the research team for all levels of the research activity. The peer(s) need to have lived experience of the research of interest.
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 | 0.287 | 0.550 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".