Librarians as Chief Diversity Officers in American Universities: A Clinical Librarian’s Experience
Bibliographic record
Abstract
This article concerns a clinical librarian's work as a chief diversity officer to promote diversity, equity, and inclusion (DEI) in healthcare and medical education at an American university. The need for healthcare professionals to have access to resources that can assist them with providing equitable care to all patients grows in tandem with the increasing significance of DEI in healthcare. These resources can only be provided by medical librarians. Medical librarians are uniquely positioned to provide these resources. From a unique perspective, a librarian serving as chief diversity officer for a multi-campus medical school in the United States can help ensure that these resources are culturally responsive and appropriate for diverse patient populations. This article discusses the benefits of appointing a clinical librarian as a chief diversity officer (CDO), promoting and increasing awareness of DEI issues through library resources, and the potential for creating more inclusive healthcare information.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.035 | 0.009 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".