Diversity training experiences and factors associated with implicit racial bias among recent genetic counselor graduates of accredited programs in the United States and Canada
Bibliographic record
Abstract
Implicit racial bias in healthcare settings can impact delivery of patient care. Exploration of this bias is necessary to improve patient experiences. We sought to understand implicit racial bias among graduates of accredited genetic counseling programs in the United States and Canada in the class of 2020 as they enter the genetics workforce and assess how this bias is associated with training and life experiences. Implicit racial bias was quantified through use of the Black-White Implicit Association Test (BW-IAT). Participants also completed an online survey focused on didactic and clinical training and personal experiences with diverse populations. Participants (n = 100) were majority White (88%), and 44% demonstrated an implicit bias favoring White individuals. Respondents reported a lack of interaction with Black healthcare professionals during their training. A concerning proportion (38%) reported experiencing or witnessing racial insensitivity perpetrated by genetic counselors or physicians in supervisory roles. Graduates reported diversity coursework as significantly less effective overall than other general genetic counseling coursework. This study reveals prevalence of implicit racial bias among genetic counselor graduates, lack of exposure to diverse populations within and outside of graduate training, and concerns regarding racial insensitivity and effectiveness of didactic and clinical genetic counseling training. Employers and program directors should implement revisions to ongoing training and graduate curriculum with consideration of these findings.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".