Needs assessment for improving library support for dentistry researchers
Bibliographic record
Abstract
OBJECTIVE: To better support dentistry researchers in the ever-changing landscape of scholarly research, academic librarians need to redefine their roles and discover new ways to be involved at each stage of the research cycle. A needs assessment survey was conducted to evaluate faculty members' research support needs and allow a more targeted approach to the development of research services in an academic health sciences library. METHODS: The anonymous, web-based survey was distributed via email to full-time researchers at the Faculty of Dentistry, University of Toronto. The survey included twenty questions inquiring about researchers' needs and behaviors across three stages of the research cycle: funding and grant applications, publication and dissemination, and research impact assessment. Data were also collected on researchers' use of grey literature to identify whether current library efforts to support researchers should be improved in this area. RESULTS: Among library services, researchers considered support for funding and grant applications most valuable and grey literature support least valuable. Researcher engagement with open access publishing models was low, and few participants had self-archived their publications in the university's institutional repository. Participants reported low interest in altmetrics, and few used online tools to promote or share their research results. CONCLUSIONS: Findings indicate that increased efforts should be made to promote and develop services for funding and grant applications. New services are needed to assist researchers in maximizing their research impact and to increase researcher awareness of the benefits of open access publishing models, self-archiving, and altmetrics.
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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 | 0.148 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".