Review of Best Practices for Diversity, Equity, and Inclusion Committees Within Colleges of Pharmacy
Bibliographic record
Abstract
Objective: To provide a review of best practices for diversity, equity, and inclusion (DEI) committees at United States Colleges of Pharmacy. Findings: DEI committees can play a crucial role in promoting a culture change in colleges of pharmacy to ensure pharmacy graduates are equipped to provide equitable and representative care for the patients they serve. There is limited literature available on DEI committee composition, role, responsibilities, and their place within a college of pharmacy’s organizational structure. A commitment to DEI should be part of the college9s strategic plan and embedded and supported at all levels of the college and university to ensure success of DEI-related strategic initiatives. For a DEI committee to be effective, its composition should be intentional to include change agents, campus leaders, and members who are passionate and knowledgeable to execute the DEI goals. For sustainable change, involvement of the entire learning community, and an organizational culture change is also important. DEI committees need to establish active bidirectional collaborations and communication with all key committees, offices, community leaders, and alumni to implement diversity goals. Summary: The DEI committee’s established place in the organizational structure of the college is essential to ensure fair and appropriate representation of the community they serve. A clearly defined DEI committee with committee composition, roles, responsibilities, and its association with all constituents of the college and community can help achieve its intended strategic goals.
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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 | 0.073 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".