Insights into genetic assistant practice and the workforce in North America
Bibliographic record
Abstract
Genetic assistant positions are now widely integrated in genetic services to address genetic counselor shortages and ultimately improve efficiency. While over 40% of genetic counselors report working with a genetic assistant ("NSGC Professional Status Survey: Work Environment," 2022), there is limited information about the genetic assistant workforce. The present study surveyed 164 genetic assistants and 139 individuals with experience working with genetic assistants (specifically genetic counselors, residents, geneticists, and administrative staff). Information was collected about genetic assistant demographics, positions, roles and responsibilities, and career paths. The data revealed that the genetic assistant workforce is demographically similar to the genetic counselor workforce and that most genetic assistants intend to pursue a career in genetic counseling. The genetic assistant positions were heterogeneous in terms of the roles and responsibilities assigned, even when separated by work setting. Lastly, participants reported that there were at least 144 genetic assistants across their institutions, a number that has likely grown since the time of the survey. The findings from this study highlight important opportunities for future research and focus, especially development of a scope of practice and competencies for genetic assistants, as well as the potential to use genetic assistant positions as an avenue to improve diversity within the genetic counseling workforce.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".