The academic librarian as co‐investigator on an interprofessional primary research team: a case study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to explore the role librarians play on research teams. The experiences of a librarian and a faculty member are situated within the wider literature addressing collaborations between health science librarians and research faculty. METHODS: A case study approach is used to outline the involvement of a librarian on a team created to investigate the best practices for integrating nurses into the workplace during their first year of practice. RESULTS: Librarians contribute to research teams including expertise in the entire process of knowledge development and dissemination including the ability to navigate issues related to copyright and open access policies of funding agencies. DISCUSSION: The librarian reviews the various tasks performed as part of the research team ranging from the grant application, to working on the initial literature review as well as the subsequent manuscripts that emerged from the primary research. The motivations for joining the research team, including authorship and relationship building, are also discussed. Recommendations are also made in terms of how librarians could increase their participation on research teams. CONCLUSION: The study shows that librarians can play a key role on interprofessional primary research teams.
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".