Systematic reviewers' perspectives on replication of systematic reviews: A survey
Bibliographic record
Abstract
Abstract Background Replication is essential to the scientific method. It is unclear what systematic reviewers think about the replication of systematic reviews (SRs). Therefore, we aimed to explore systematic reviewers' perspectives on (a) the definition and importance of SR replication; (b) incentives and barriers to conducting SR replication; and (c) a checklist to guide when to replicate an SR. Methods We searched PubMed for SRs published from January to April 2021, from which we randomly allocated 50% to this survey and 50% to another survey on data sharing in SRs. We sent an electronic survey to authors of these SRs (n = 4669) using Qualtrics. Quantitative responses were summarized using frequency analysis. Free‐text answers were coded using an inductive approach. Results The response rate was 9% (n = 409). Most participants considered “replication of SRs” as redoing an SR (68%) or reanalyzing originally collected data (61%), using the same or similar methods. Participants also considered updating an SR, either one's own (42%) or others (43%), equivalent to replication. Most participants agreed that replication of SRs is important (89%). Although 54% of participants reported having conducted a replication of a SR, only 22% have published a replication within 5 years. Those who published a replication (n = 89) often found their replication supported (47%) or expanded the generalizability of the original review (51%). The most common perceived barriers to replicating SRs were difficulty publishing (75%), less prestige (65%), fewer citations (56%), and less impact on career advancement (55%) compared to conducting an original SR. A checklist to assess the need for replication was deemed useful (79%) and easy to apply in practice (69%) by participants. Conclusion Reviewers have various perceptions of what constitutes a replication of SRs. Reviewers see replication as important and valuable but perceive several barriers to conducting replications. Institutional support should be better communicated to reviewers to address these perceptions.
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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 | 0.849 | 0.935 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.029 | 0.029 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.012 | 0.015 |
| 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".