Roles for librarians in systematic reviews: a scoping review
Bibliographic record
Abstract
Objective: What roles do librarians and information professionals play in conducting systematic reviews? Librarians are increasingly called upon to be involved in systematic reviews, but no study has considered all the roles that librarians can perform. This inventory of existing and emerging roles aids in defining librarians’ systematic reviews services.Methods: For this scoping review, the authors conducted controlled vocabulary and text-word searches in the PubMed; Library, Information Science & Technology Abstracts; and CINAHL databases. We separately searched for articles published in the Journal of the European Association for Health Information and Libraries, Evidence Based Library and Information Practice, the Journal of the Canadian Heath Libraries Association, and Hypothesis. We also text-word searched Medical Library Association annual meeting poster and paper abstracts.Results: We identified 18 different roles filled by librarians and other information professionals in conducting systematic reviews from 310 different articles, book chapters, and presented papers and posters. Some roles were well known such as searching, source selection, and teaching. Other less documented roles included planning, question formulation, and peer review. We summarize these different roles and provide an accompanying bibliography of references for in-depth descriptions of these roles.Conclusion: Librarians play central roles in systematic review teams, including roles that go beyond searching. This scoping review should encourage librarians who are fulfilling roles that are not captured here to document their roles in journal articles and poster and paper presentations. This article has been approved for the Medical Library Association’s Independent Reading Program.
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.
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 | 0.648 | 0.734 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.049 | 0.049 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.013 | 0.009 |
| 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 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".