CHLA 2024 Conference Lightning Talks / ABSC Congrès 2024 Présentations Éclair
Bibliographic record
Abstract
Introduction: This is a follow-up study to a previous bibliometric analysis that analyzed the scale of librarian involvement in Systematic Reviews (SRs) at the University of Alberta.The intention of this project is to provide a Canada-wide analysis of health librarian involvement in SRs.There are several implications for this work; 1) training at a national level; 2) mentorship and coaching opportunities; 3) using librarian involvement as an indicator of quality SR publications, it could tell us whether or not Canadian SR publications are rigorously adhering to PRISMA-S and other reporting guidelines.Methods: Using Web of Science (WoS), we searched for SRs completed in the past five years.Systematic reviews identified through WoS will be screened in two phases: 1) Determining if the paper is a true SR publication (e.g.excluding duplicates, protocols, systematic review methodology papers, etc.), 2) Screening for librarian involvement (co-author, acknowledgement, or no involvement).Results: Of the 9514 studies retrieved, 7965 records advanced to full-text screening.A random sample of 400 references was pulled for data extraction.Of the 400 publications, 49 (12%) had a librarian co-author, 77 (19%) formally acknowledged librarians in the acknowledgements section, and 132 (33%) mentioned librarian support in the full-text of the paper.Discussion: This study will demonstrate the great deal of variation of how the work of librarians is reflected in SRs at a national scale.Continuing to educate researchers about the work of librarians is crucial to fully represent the value librarians bring to systematic reviews. LT2. Bringing order to chaos: how a work plan can help librarians support users doing systematic reviews and scoping reviews
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.506 | 0.279 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".