Diversity, equity, and inclusion initiatives for health and medical library workers: A scoping review
Bibliographic record
Abstract
BACKGROUND: Health disparities remain as a systemic challenge. With the emergence of the Black Lives Matter movement and scant evidence of diversity, equity, and inclusion (DEI) initiatives for workers in health science libraries, this scoping review maps evidence that can be incorporated into a culture of change. OBJECTIVES: To identify the extent, type, and location of DEI initiatives being conducted in health science libraries for library workers. METHODS: Eight databases were systematically searched for literature from 2014 onwards, including PubMed, Scopus, and Web of Science. Four reviewers were involved in screening and data extraction. RESULTS: Reviewers excluded 6712 title/abstracts. A total of 177 articles progressed to full-text screening, where 153 were excluded. The final number of articles that underwent data extraction was 24. DISCUSSION: Initiatives primarily occurred in academic libraries, led by library workers. Identities mostly focused on were gender, race, and sexuality, while some initiatives focused on general DEI concepts. Most literature pertained to library patrons, demonstrating a gap in reported initiatives for health science library workers. Assessment of initiatives was lacking, with no validated assessment tools used. All of the articles focused on either the United States or Canada. CONCLUSION: Diversity continues to be a challenge within the profession; this should be mitigated through recruitment and retention strategies along with mentorship for new and diverse librarians.
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 | 0.045 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".