Librarian Co-Authored Systematic Reviews are Associated with Lower Risk of Bias Compared to Systematic Reviews with Acknowledgement of Librarians or No Participation by Librarians
Bibliographic record
Abstract
Abstract Objective - To explore the prevalence of systematic reviews (SRs) and librarians’ involvement in them, and to investigate whether librarian co-authorship of SRs was associated with lower risk of bias. Methods - SRs by researchers at University of Oslo or Oslo University Hospital were counted and categorized by extent of librarian involvement and assessed for risk of bias using the tool Risk of Bias in Systematic Reviews (ROBIS). Results - Of 2,737 identified reviews, 324 (11.84%) were SRs as defined by the review authors. Of the 324 SRs, 4 (1.23%) had librarian co-authors, in 85 (26.23%) librarians were acknowledged or mentioned in the methods section. In the remaining 235 SRs (72.53%), there was no clear evidence that a librarian had been involved. Librarian co-authored SRs were associated with lower risk of bias compared to SRs with acknowledgement or no participation by librarians. Conclusion - SRs constitute a small portion of published reviews. Librarians rarely co-author SRs and are only acknowledged or mentioned in a quarter of our sample. The quality and documentation of literature searches in SRs remains a challenge. To minimise the risk of bias in SRs, librarians should advocate for co-authorship.
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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 | 0.255 | 0.724 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".