Expert searcher, teacher, content manager, and patient advocate: an exploratory study of clinical librarian roles
Bibliographic record
Abstract
OBJECTIVE: The research explored the roles of practicing clinical librarians embedded in a patient care team. METHODS: Six clinical librarians from Canada and one from the United States were interviewed to elicit detailed descriptions of their clinical roles and responsibilities and the context in which these were performed. RESULTS: Participants were embedded in a wide range of clinical service areas, working with a diverse complement of health professionals. As clinical librarians, participants wore many hats, including expert searcher, teacher, content manager, and patient advocate. Unique aspects of how these roles played out included a sense of urgency surrounding searching activities, the broad dissemination of responses to clinical questions, and leverage of the roles of expert searcher, teacher, and content manager to advocate for patients. CONCLUSIONS: Detailed role descriptions of clinical librarians embedded in patient care teams suggest possible new practices for existing clinical librarians, provide direction for training new librarians working in patient care environments, and raise awareness of the clinical librarian specialty among current and budding health information professionals.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".