Legal approaches to risk of harm in genetic counseling: perspectives from Quebec and Qatar
Bibliographic record
Abstract
Genetic counseling is a fast-growing profession worldwide, with genetic counselors taking on increasingly comprehensive and autonomous roles in the healthcare sector. However, the absence of appropriate legal frameworks could potentially create risks of harm to the public. Legal recognition serves to protect the public from risk of harm by regulating the safe and competent practice of healthcare professionals. Genetic counseling is not legally recognized in most world jurisdictions. Examination of the legal status of genetic counseling in different jurisdictions and whether existing legal mechanisms are adequate to address potential risks of harm is therefore timely. This paper examines the different roles of genetic counselors in the Canadian province of Quebec and the state of Qatar, the authors' respective jurisdictions. It considers the types of harms that may be created where appropriate legal mechanisms are lacking, considering the socio-political and legal differences between the two jurisdictions. Moreover, it examines the legal status of genetic counseling in Quebec and Qatar to determine whether these statuses appropriately address the identified risks of harm. The authors argue that existing legal frameworks are inadequate to address these risks and recommend that additional regulatory mechanisms be implemented to properly protect the public from risks of harm.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".