The role of subject knowledge in academic health sciences libraries: an online survey of librarians working in the United States
Bibliographic record
Abstract
Introduction - Previous research suggests that Canadian academic health sciences librarians value knowledge of the health sciences and spend a considerable amount of time gaining and maintaining it. The current study replicates the earlier Canadian survey but employs a larger American sample to address three questions: Do academic health sciences librarians working in the United States find knowledge of the health sciences to be important, and if so, how do they acquire it? Do the attitudes of Canadian and American academic health sciences librarians differ with respect to subject knowledge? Methods - An invitation to participate in a Web-based survey was sent to 711 academic health sciences librarians working in the US; 154 participated. Results - Academic health sciences librarians in the US felt that keeping up with the scientific and medical literature was important to doing their jobs, although only 50% of respondents felt that a degree in the health sciences was somewhat or very useful. Discussion - Participating in professional organizations, visiting Web sites, and reading or browsing journals or magazines were rated by respondents as the best ways to become informed about the health sciences. Findings were similar to those of an earlier survey of Canadian academic health sciences librarians.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".