Genetic counseling job market in the United States and Canada: An analysis of job advertisements 2014–2016
Bibliographic record
Abstract
Abstract Genetic counseling careers continue to evolve, yet there remains a lack of information about hiring trends in the genetic counseling profession. In this study, job advertisements in the United States and Canada were analyzed, using the National Society of Genetic Counselors (NSGC) Job Connections and the American Board of Genetic Counseling (ABGC) eBlasts from 2014 to 2016 to appraise job roles, qualifications, settings, specialties, and type. NSGC had 1875 advertised openings from 2014 to 2016, while ABGC had 373 advertised openings. Jobs containing a “counseling” role increased as a percentage from 2014 to 2016 when advertised by NSGC (χ2 = 25.52, p < 0.000001) but decreased each year from 2014 to 2016 as a percentage when advertised through ABGC (χ2 = 14.29, p = 0.0008). In the ABGC job postings, it was noted that 36% of job postings were advertised for other specialties (not solely cancer, pediatric, or prenatal) in 2014, and increased to 67% in 2016 (χ2 = 10.09, p = 0.02). Examining the job specialties posted by ABGC and NSGC, several new or unique roles were found in the job advertisements such as ophthalmology counselor, variant curator, rare diseases information specialist, and clinical policy analyst. Roles for temporary, contract or fellowship positions are possibly becoming more common, along with small upturns in positions that are off‐site or remote. In analyzing the changing workforce, there was a statistically significant decrease identified in jobs advertised by NSGC in the laboratory setting from 28% in 2014 to 17% in 2016 (χ2 = 24.12, p = 0.000024). This information on the evolving career of genetic counseling is valuable for the current workforce and training programs as they adapt with the changing landscape of the profession.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".