Extending medical librarians’ competencies to enhance collection organisation
Bibliographic record
Abstract
BACKGROUND: Like many health library associations, the Medical Library Association (MLA) developed competencies guiding lifelong learning and competence for medical librarians. Medical librarians should be able to develop skills in identified areas. One MLA indicator of organising resources defines expert skill as the ability to develop classification and metadata schemes for unique collections. OBJECTIVES: This manuscript reviews available curricula for selected library programmes in the United States and Canada, along with professional development and informal opportunities for skill development to identify how medical librarians, who are not experts in cataloging or metadata and not employed as cataloging or metadata librarians, can progress in competency. METHODS: The authors reviewed library school and continuing education programming around metadata, along with answers from a pre-existing informal poll regarding cataloging and metadata roles in health sciences libraries. Data were collected and examined using descriptive statistics. DISCUSSION: Gaps and opportunities for education around organising resources are discussed, including library school courses, formal continuing education opportunities and informal learning (e.g. peer support networks, on-the-job learning). CONCLUSION: Education in organising resources should be created throughout the educational journey of librarianship. Continuing educational opportunities in organising resources should be created by professional organisations that expect competency in this area.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".