An analysis of growth in the genetic counseling profession 2009 to 2019.
Bibliographic record
Abstract
e13526 Background: Genetic counselors (GCs) are health care professionals who provide support to patients and physicians navigating the rapidly changing landscape of genetic testing and the genetic underpinnings of disease. Increased demand for genetic counseling services prompted an analysis of changes in the workforce over the last decade. Methods: To quantify the growth in the GC profession in the U.S and Canada in the last decade, we acquired data from the American Board of Genetic Counseling, National Society of Genetic Counselors, Canadian Association of Genetic Counselors, Accreditation Council for Genetic Counseling and Association of Genetic Counseling Program Directors. Results: Between 2009 and 2019, the workforce more than doubled, growing from 2,205 ABGC-certified GCs to 5,172. In Canada, the number of CAGC-certified GCs has grown from 211 in 2009 to 327 in 2019. Growth is striking in cancer genetic counseling; the proportion of GCs providing direct patient care in North America who report cancer as a primary specialty has increased from 25% in 2008 to 50% in 2019. Similar growth has been seen in training opportunities for GCs. The number of accredited graduate programs has increased from 33 in 2009 to 51 in 2019, with several more in development. Combined, these programs had 464 training slots in 2019, up from 223 in 2009. In 2019, 1569 applicants registered for the applicant match for training. Training opportunities and clinical genetic counselors are concentrated in large metropolitan areas, with over half of GCs working in 28 metro regions. GC services in rural areas are increasingly provided remotely via telemedicine, with 59% of GCs in direct patient care in 2018 reporting providing services by phone and 19% using web or video services to deliver care. In cancer genetics, about 50% of GCs nationwide reported in 2018 their 3rd next available appointment for new patients was within 14 days. Conclusions: The past decade has seen significant growth in the numbers of GCs and more patients have access to GCs than a decade ago. Reimbursement for services remains a significant barrier to access. Further research is warranted to understand additional political, administrative and logistical facilitators and barriers to providing care to all who need genetics services.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".