Bibliographic record
Abstract
Introduction: The demand for systematic reviews (SR) in research intensive health related departments is rapidly increasing, in both clinical and academic settings.In response to this, the role of the liaison librarian is changing from an advising, supportive role to being an integral part of the research process and a member of the research team.This study aimed to determine the existing awareness and level of expectation of librarian involvement in the systematic review process of the researchers within the University of Waterloo health science faculties and schools.Methods: From the summer of 2013 to early 2014, four University of Waterloo health librarians delivered a survey to faculty and PhD students within the Faculty of Applied Health Sciences, School of Optometry and School of Pharmacy.The survey solicited data on their current and future systematic review work.Results: Survey feedback from faculty displayed a wide range of systematic review experience and awareness of potential librarian involvement.PhD feedback is currently being collected, with data analysis to follow.Discussion: This was an extremely valuable process.UW librarians gained knowledge of the health researcher expectations of librarian support and identified multiple ways to meet their needs.It also served to advertise the potential role of the librarian beyond traditional activities and how our expertise can be used towards knowledge creation and synthesis.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.350 | 0.166 |
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".