Changes in Diversity, Equity, and Inclusion Activities of Family Medicine Departments
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Institutional racism causes worse health outcomes for patients of racial/ethnic minority groups via limited access to health care, disparities in quality of care delivered, and lack of physician diversity. Increased attention to racism in 2020 led many medical institutions to examine their diversity, equity, and inclusion (DEI) efforts. In the context of increased national attention to health equity, this study sought to investigate the current status of DEI infrastructure by evaluating leadership and support related to DEI in family medicine departments in 2020 and 2021. METHODS: We analyzed department and chair characteristics as well as departmental DEI infrastructure (ie, leadership and actions) from Association of Departments of Family Medicine survey data in 2020 (data collected from June to September 2020) and 2021 (data collected from September to December 2021). We performed multiple regression analyses to evaluate whether department characteristics or specific DEI activities were associated with increased DEI infrastructure in 2021 compared to 2020. RESULTS: Of the 165 department chairs sent the survey in both 2020 and 2021, 56 (33.9%) responded both years. Departments with a designated DEI leader increased from 42.9% in 2020 to 60.7% in 2021, but about 40% of departments lacked key supports for this position (ie, funding, staff support, and a pathway for advancement). Regression analysis did not demonstrate associations between independent variables and three measures of departmental DEI activities. CONCLUSIONS: This study demonstrates that designated leadership for DEI work increased in family medicine departments between 2020 and 2021.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.010 |
| 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".