Genetic counseling training program perspectives on delivering disability‐related education
Bibliographic record
Abstract
Ensuring genetic counselors are educated about disability is crucial due to the societal implications of genetic testing and the support they provide to clients. Research indicates that genetic counselor preparedness in these areas is both limited and variable, with practitioners and disability advocates expressing desire for more robust disability-related curricula. This study aimed to gain updated information and perspectives on relevant curricula currently offered at accredited genetic counseling training programs. With future goals of filling in curricula gaps, we also investigated program leadership interest in a shared disability-related curricula resource. Leadership from 16 out of 51 accredited genetic counseling training programs in the United States and Canada responded to our survey. We found that current disability-related curricula are often based in classroom didactics, which is more likely to be focused on medical aspects of disability, whereas community-based education is more likely to expose students to community support resources and lived experiences. As such, our study highlights the variability of genetic counselor training about disability and gaps in community-based education. To fill in these gaps, we found that all programs expressed interest in a curricula development resource.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".