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Record W4385349801 · doi:10.3389/fgene.2023.1190421

Legal approaches to risk of harm in genetic counseling: perspectives from Quebec and Qatar

2023· review· en· W4385349801 on OpenAlexaffabout
Dimitri Patrinos, Mohammed Ghaly, Mashael Al‐Shafai, Ma’n H. Zawati

Bibliographic record

VenueFrontiers in Genetics · 2023
Typereview
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsMcGill University Health Centre
FundersQatar National Research FundFonds National de la Recherche Luxembourg
KeywordsHarmGenetic counselingPolitical sciencePublic healthBusinessMedicinePublic relationsLawNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.148
GPT teacher head0.368
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2023
Admission routes2
Has abstractyes

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