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
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
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.
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 | 0.059 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.085 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it