Acknowledging Librarians’ Contributions to Systematic Review Searching
Bibliographic record
Abstract
Abstract Introduction Academic health librarians are increasingly involved as members of research teams that conduct systematic reviews. Sometimes librarians are co-authors on the resulting publications, sometimes they are acknowledged, and sometimes they receive no recognition. This study was designed to query librarian supervisors’ understanding of the extent to which Canadian academic health librarians are involved in systematic reviews and the manner in which their work is recognized. Methods A survey asking 21 questions was sent to supervisors of librarians at all 17 academic health sciences libraries in Canada, querying the extent and nature of librarians’ involvement in systematic review research projects and the forms of acknowledgement that they receive. Results Fourteen responses to the survey were received. Results show strong expectations that librarians are involved, and will be involved, in systematic review research projects. Results related to the number of reviews undertaken, the amount of time required, the forms of acknowledgement received, and the professional value of systematic review searching varied greatly. Discussion The lack of consensus among academic health librarians’ supervisors regarding most aspects of librarians’ involvement in systematic review projects, and the ways in which this work is and should be acknowledged, points to the need for research on this subject.
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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 | 0.671 | 0.867 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".