Genetic counselors and legal recognition: A made‐for‐Canada approach
Bibliographic record
Abstract
Genetic counseling is a fast-growing profession in Canada. Yet, despite its growth, genetic counseling lacks legal recognition in the majority of Canadian provinces. Legal recognition serves to regulate professions, including genetic counseling, that if not properly regulated, expose the public to the risk of harm. Under Canadian law, there are three models of legal recognition: 1) the constitution of a professional order, 2) inclusion in a professional order, and 3) delegation. This paper explores the practical implications of these different models of legal recognition for genetic counselors. It focuses on the balancing act between protecting the public and the resources required to seek legal recognition under the three different models. With a small number of genetic counselors (n = 484, with 89% found in 4 provinces) compared to other professions, the route toward professional regulation for genetic counselors can be challenging. Though legal recognition occurs at the provincial rather than federal level in Canada, we nonetheless advocate for pan-Canadian discussions that may benefit future pursuits of legal recognition.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".