Librarian Involvement in Systematic Reviews at Queen’s University: An Environmental Scan
Bibliographic record
Abstract
Introduction Systematic reviews pose a growing research methodology in many fields, particularly in the health sciences. Many publishers of systematic reviews require or advocate for librarian involvement in the process, but do not explicitly require the librarian to receive co-authorship. In preparation for developing a formal systematic review service at Queen’s, this environmental scan of systematic reviews was conducted to see whether librarians receive co-authorship or other acknowledgement of their role in systematic reviews. Methods A search of the Joanna Briggs Database and both Medline and PubMed for systematic reviews with at least one Queen’s-affiliated author was completed. These were classified based on the level of acknowledgement received by the librarian involved in the search into three groups: librarian as co-author, librarian acknowledged and unclear librarian involvement. In instances where the lead author was Queen’s-affiliated, these were also categorized by their primary academic department. Results Of 231 systematic reviews published with at least one Queen’s-affiliated author since 1999, 32 listed a librarian as co-author. A librarian received acknowledgement in a further 36. The School of Nursing published the most systematic reviews and was most likely to have a librarian as co-author. Discussion Librarians at Queen’s are actively involved in systematic reviews and co-authorship is a means of valuing our contribution. Librarians appear to be more likely to achieve co-authorship when they have advocated for this role in the past. Success varies according to the cultural norms of the department.
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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 | 0.320 | 0.611 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.049 | 0.103 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".