The State of Clinical Librarianship in Canada: a Review of the Literature, 1970–2013
Bibliographic record
Abstract
This paper examines the history of clinical librarianship in Canada from 1970 to 2013 as seen through the lens of practitioner narratives and published literature. While no reviews of clinical librarianship in Canada were found in the literature search, there were many project descriptions in articles and published reports that have provided insight into the field during its formative period in Canada from the 1970s. In addition to tracing narrative histories from 1970 to 2013, the author has continued to wonder why these important stories have never properly been told. Was it because the scope of clinical librarianship, its expected and embodied professional duties, was not regulated (as it is in the United States and United Kingdom)? Is it because the American Library Association accredited library schools in Canada do not offer appropriate curricula and professional training? It seems clear that some librarians in Canada were pioneers in the way that Gertrude Lamb was in the United States, but they did not call themselves clinical librarians. Consequently, they opted for more generic job titles such as medical librarian and health librarian. Whatever the reasons for this, it is within this framework that the author begins an exploration of clinical librarianship in Canada. The paper's aim is to provide a view into clinical librarianship in Canada back to the 1970s to ensure the story is properly told.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.077 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".