Research Support in Health Sciences Libraries: A Scoping Review
Bibliographic record
Abstract
Background:As part of a health sciences library’s internal assessment of its research support services, an environmental scan and literature review were conducted to identify research services offered elsewhere in Canada. Through this process, it became clear that a more formal review of the academic literature would help libraries make informed decisions about their services. To address this gap, we conducted a scoping review of research services provided in health sciences libraries contexts.Methods:Searches were conducted in Medline, Embase, ERIC, CINAHL, LISTA, LISS, Scopus, Web of Science, Google Scholar and Google for articles which described the development, implementation, or evaluation of one or more research support initiatives in a health sciences library context. We identified additional articles by searching reference lists of included studies and soliciting medical library listservs.Results:Our database searches retrieved 7134 records, 4026 after duplicates were removed. Title/abstract screening excluded 3751, with 333 records retained for full-text screening. Seventy-five records were included, reporting on 74 different initiatives. Included studies were published between 1990 and 2017, the majority from North American and academic library contexts. Major service areas reported were the creation of new research support positions, and support services for systematic review support, grants, data management, open access and repositories.Conclusion:This scoping review is the first review to our knowledge to map research support services in the health sciences library context. It identified main areas of research service support provided by health sciences libraries that can be used for benchmarking or information gathering purposes.
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 | 0.077 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.060 | 0.078 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".