Genetic Counseling Practice in Next Generation Sequencing Research: Implications for the Ethical Oversight of the Informed Consent Process
Bibliographic record
Abstract
The potential for next generation sequencing research (NGS) to generate individual genetic results could have implications for the informed consent process and the provision of genetic counseling. We undertook a content analysis of informed consent templates and guidelines produced by Canadian institutional review boards, purposively sampling documents used by researchers to obtain consent from participants in genetics studies. Our goal was to examine the extent to which the informed consent documents addressed genetic counseling and the return of individual genetic results. Our analysis reveals that the majority of informed consent documents did not mention genetic counseling while several did not mention the return of results. We found differences in the ways in which documents addressed availability of counseling, eligibility criteria for referral to a genetic counselor, genetic counselor involvement, provision of services to family members of participants and incidental findings. From an ethical standpoint, consent documents should provide appropriate information so that participants may make an informed decision about their participation in research. The need to ensure adequate counseling for study populations in an NGS research context will necessarily involve adapting values that underlie care in genetic counseling practice. If the interests of research participants are to be truly promoted, the drafting and review of informed consent documents should give proper due to genetic counseling.
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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 | 0.811 | 0.807 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.025 | 0.112 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.023 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 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".