Bibliographic record
Abstract
Examinations of the experiences of librarians with a health sciences background have appeared in the library literature over the past decade. Fikar's 2001 survey was perhaps the first article written about this topic. The majority of respondents to Fikar's survey felt that their background was beneficial or would be beneficial to their librarianship career [1]. Watson's survey of a small sample of Canadian academic health sciences librarians found that the 21 of 30 (70%) respondents felt that having a health sciences degree was not very important or not at all important to a health sciences librarian career [2]. As opposed to this, a 2007 survey of British health librarians found that those with a science background said their degree “gave them more confidence with the terminology and general subject background.” Those without a health sciences degree emphasized “transferable skills or other management skills” as important rather than their background. Most felt there was a need for “better subject knowledge” [3]. The current survey sought to identify why those librarians who had a health sciences background chose librarianship and if they felt their health sciences background was advantageous in working as a health sciences librarian.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".