The Canadian genetic counseling workforce: Perspectives from employers and recent graduates
Bibliographic record
Abstract
There is a lack of literature devoted to the Canadian genetic counseling workforce. Current and prospective genetic counseling students, graduates, employers, programs, and funding agencies remain largely uninformed of trends in the job market. The purpose of this study was to investigate the employment experiences of recent Canadian genetic counseling graduates and employers of genetic counselors (GCs) in Canada. An online survey was distributed to Canadians who had graduated from North American genetic counseling programs from 2014 to 2018 and to employers with experience hiring patient-facing genetic counselors from 2016 to 2018. Quantitative data were analyzed through descriptive statistics and, where appropriate, logistic regression. Qualitative data were reviewed to illustrate and support the quantitative data. Half of the employer responders (11/22) reported hiring more or many more genetic counselors compared to 3 years ago (2016), and a majority (n = 19, 86%) reported a desire to hire at least one more genetic counselor if funding were available. Most graduates (45/70, 64%) reported securing employment before graduation, 33% (n = 23) within 6 months of graduation, and no one taking longer than 1 year. For recent graduates, location was the most important factor when looking for work, and those who chose to work in Canada experienced significantly more challenges gaining employment as opposed to those who worked internationally (p = 0.03). The specialties in highest demand for genetic counselors and the most common areas of practice for recent graduates included adult general genetics, prenatal genetics, and cancer genetics. Overall, our findings suggest that there is a growth of employment opportunities in Canada and more employer-reported need for clinical genetic counselors; however, there is a lack of funding to support this expansion.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".