Health Sciences and Beyond: An Investigation into Canadian Librarian Participation in Systematic Reviews Across Disciplines
Bibliographic record
Abstract
The aim of this survey was to describe academic librarian roles in systematic reviews (SR) in any discipline, as a follow-up to a previous survey of Canadian academic health sciences librarians. A convenience sample of librarians at Canadian universities who support SRs were invited to complete a survey. Respondents were asked about their roles, training, knowledge, and barriers to providing SR services. Ninety-four librarians responded to the survey. The most common roles were in the literature search; time and training were the most frequently reported barriers. Librarians are supporting reviews in multiple, diverse disciplines, primarily as expert searchers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchScholarly communication Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.084 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".