Training a New Librarian in the What, How, Where, and Why of Health Sciences Collection Management
Bibliographic record
Abstract
Collection management for the health sciences, particularly clinical medicine, is an increasingly complex job which, anecdotally, is usually given to experienced librarians.Health sciences libraries tend to delegate collections responsibilities to one librarian who holds all of the institutional collections knowledge.Replacing these people as they retire or move on can be difficult unless new librarians become trained in collections work.At the Michigan State University Libraries, recent search committee experience revealed that an entrylevel health sciences collections position attracted fewer applicants than entry-level health sciences positions for instruction, liaison, or educational technology.This may reflect the focus of library school curricula as even applicants for the collections position generally had very little relevant exposure to the subject in library school or internships.Health sciences librarianship in general can involve a lot of on-the-job training, but supervisors hiring new librarians for collections may find themselves starting from scratch.This poster will demonstrate a detailed training program developed to teach a newly graduated librarian how to develop and manage an extensive clinical medicine collection at a large university library serving medical schools.The step-wise approach focuses on learning by doing, moving from the specific to general principles rather than the other way around.Decision making for selection of materials is approached from multiple angles: institutional analysis, subject analysis, and publisher and vendor knowledge.The new librarian will provide insight into which parts of the training were most helpful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".