Risk for Patient Harm in Canadian Genetic Counseling Practice: It's Time to Consider Regulation
Bibliographic record
Abstract
With the increasing awareness of genetic contributions to disease in Canada, the availability of and demand for genetic testing has soared. Genetic counseling is becoming a recognized and rapidly growing (yet unregulated) health profession in Canada. We hypothesized that the potential risk for harm to the public posed by genetic counseling practice in the province of Ontario is sufficient to consider regulation. The Ontario Ministry of Health and Long-Term Care (MOHTLC) sets criteria (both primary and secondary) to identify health professional bodies that meet the threshold for regulation in the province. We developed a survey based on the MOHTLC criteria to determine if genetic counselors meet the primary criteria to be considered for health professions regulation in Ontario. We surveyed 120 Ontario genetic counselors about their clinical practice and perceptions of risk for harm to the public. Results indicate that Ontario genetic counselors are highly independent in their clinical practice and are involved in patient care activities, clinical judgement and decision-making that have the potential to harm patients. In particular, cancer genetic counselors were identified as a cohort that practices with relatively high autonomy and low supervision. In summary, our study indicates that genetic counseling practice in Ontario meets the primary criteria to be considered for regulation.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".