Librarian Support for Researchers in Ontario Hospitals
Bibliographic record
Abstract
Introduction This study investigates the extent to which Ontario hospital librarians and library resources support researchers and describe the librarians' participation in research capacity building within their institutions. Methods A 16-question SurveyMonkey™ web-based survey questionnaire was disseminated via email to 53 potential participants consisted of library directors, managers and solo librarians. Results The response rate was 60%. The number of researchers supported by librarians ranged from 10 or less to 76 or more researchers in the past 10 months. Librarians supported a variety of scholarly research outputs, assisting researchers with journal articles being the most frequently supported activity. The top three library resources used to support researchers were licensed electronic journals, print collections and expert librarian searches. One of the reported ways librarians received training to better assist researchers was via online continuing education.Discussion As others have reported, there was a predominance of support for literature studies including literature reviews and systematic reviews. Surprisingly, some librarians reported that they had all the databases or resources they needed to support research, while an alarming 79 per cent of respondents reported not having access to all the databases and resources they needed. Lack of access to databases or online resources may have a negative effect on the quality of research the librarians provided. Raising the awareness of the role of the librarian in supporting researchers in the hospital setting can inform the health sciences librarians' professional practices and provide evidence of the library's participation in the research capacity building of the organization.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".