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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".