Further defining the roles and impact of genetic counselors in the biotechnology and pharmaceutical industry
Bibliographic record
Abstract
As personalized medicine has gained traction, drug development models in the biotechnology and pharmaceutical industry (BPI) have increasingly sought to address medical conditions with a genetic component, creating an opportunity for genetic counselors (GCs) to fill new roles and utilize their unique training to contribute to drug development. Despite the potential for GCs in BPI, literature around the role of GCs in this industry has been limited. Our mixed methods study aimed to assess how the roles of GCs in BPI have evolved since 2016, investigate the value of and opportunity for GCs in this industry, and further characterize their motivation and job satisfaction. Participants were recruited via social media advertising, snowball sampling, and email listservs from the National Society of Genetic Counseling (NSGC), the Canadian Association of Genetic Counselors (CAGC), and the American Board of Genetic Counseling (ABGC). Survey (n = 20) and interview (n = 6) data indicates many aspects of GC roles in BPI are consistent with the 2016 study. However, there is evidence of roles becoming more varied and with increasing recognition of the value of GCs, opportunities for involvement in BPI are growing. Furthermore, combined study data found that GCs are motivated by the flexibility of BPI roles as well as the opportunity to contribute to rare disease treatment development and that they are overall satisfied with most aspects of their jobs. Interview data also found that genetic counseling training has the potential to improve clinical trial design and outcomes by making drug development more patient-centric. Finally, combined study data found that while GCs continue to utilize Accreditation Council of Genetic Counseling (ACGC) practice-based competencies (PBCs), business-related training may benefit GCs seeking to enter BPI. Together, these findings are critical for informing genetic counseling training programs, employers within BPI, and GCs interested in entering these positions.
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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 | 0.001 | 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".