An environmental scan of librarian involvement in systematic reviews at Queen’s University: 2020 update
Bibliographic record
Abstract
Introduction: Systematic reviews are a growing research methodology in the health sciences, and in other disciplines, having a significant impact on librarian workload. In a follow up to an earlier study, an environmental scan was conducted at Queen's University to determine what has changed, if anything, since the introduction of a tiered service for knowledge synthesis by examining review publications where at least one co-author was from Queen's University. Methods: A search was conducted in PubMed and the Joanna Briggs database to find systematic reviews and meta-analyses with at least one author from Queen's University for the five-year time since the last environmental scan. Reviews were categorized by the degree of involvement of the librarian(s) regardless of their institutional affiliation: librarian as co-author, librarian named in the acknowledgements, no known librarian involvement in the review. Results: Of 453 systematic reviews published in the five-year time frame, nearly 20% (89) had a librarian named as co-author. A further 24.5% (110) acknowledged the role of a librarian in the search, either in the acknowledgements section or in the body of the text of the article. In just over half of reviews (235 or 51.8%) a librarian was either not involved, or was not explicitly acknowledged. More librarians and more institutions were represented in the period of 2016-2020 than in 2010-2015. Conclusion: In the five years since the last environmental scan, an increasing number of reviews recognized the role of the librarian in publishing systematic reviews, either through co-authorship or named acknowledgement. This also suggests that as more librarians have become involved in systematic reviews, librarian capacity for this work has increased compared to five years ago.
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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 | 0.194 | 0.455 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.067 | 0.086 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".