A cross‐sectional survey‐based exploration of diversity in the admissions committees and student cohorts of genetic counseling programs over time
Bibliographic record
Abstract
As of 2022, 89% of genetic counselors report being White, and 93% report being women. We examined diversity in genetic counseling (GC) program admission committees (ACs-who are responsible for deciding who will make up the future GC workforce) and student cohorts to understand the impact of recent diversification efforts, and where future work should be focused. One representative from each AC of the 57 accredited GC programs in North America in 2022 was invited to participate in a cross-sectional survey to provide information on the diversity of GC ACs and student cohorts between 2019 and 2022 for the following dimensions: race/ethnicity, gender, sexual orientation, disability status, neurodiversity, and rural or low socioeconomic status backgrounds. Members of 38/57 (67%) ACs participated. Using the Cochran-Armitage test for trends, significant increases were observed for the proportion of individuals of a racial/ethnic minority within ACs (from 9% in 2019 to 18% in 2022; p < 0.0001). There was no change for other minoritized social identities. There was no significant change over time in the proportion of students holding any of the minoritized social identities. A low correlation was found between the diversity of ACs and student cohorts. This study reaffirms the need for greater diversification efforts within ACs and student cohorts. Increased transparency about the social identities of AC members and about ACs' commitment to diversification may facilitate the diversification of the profession.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".