Strategies and Barriers for Diversity, Equity, Inclusion, and Antiracism Work in Family Medicine Departments: A CERA Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Medical schools and family medicine organizations have been working on advancing diversity, equity, inclusion, and antiracism (DEIA). Black, Indigenous, and People of Color (BIPOC) faculty members are disproportionately expected to lead DEIA initiatives, negatively affecting academic promotion and well-being. Our study aimed to describe the existing DEIA initiatives, strategies, and barriers to implementing support for DEIA work, as well as the implications of addressing the minority tax in US and Canadian family medicine departments. METHODS: We used data collected as a part of the 2023 Council of Academic Family Medicine Educational Research Alliance (CERA) study. The survey was delivered to 227 department chairs across the United States and Canada. RESULTS: The survey response rate was 50.2% (114/227). Sixty-two percent of the respondents strongly agreed that advancing DEIA was important, and 55.4% reported having a DEIA leader, with 75.4% of those positions reportedly held by BIPOC faculty. Lack of funding was identified as the most significant barrier (26.2%), followed by lack of faculty expertise (18.7%). Department chairs who strongly agreed that DEIA work was important were significantly more likely to report having a DEIA committee, mentorship for BIPOC faculty, and a holistic review for faculty recruitment than those who did not strongly agree. CONCLUSIONS: Though most department chairs perceived advancing DEIA work as important, appropriate compensation and institutional support are often lacking. Further study is needed to explore ways in which departments can enhance their institutional support for DEIA initiatives.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".