North American genetic counselors' approach to collecting and using ancestry in clinical practice
Bibliographic record
Abstract
Current guidelines from the National Society of Genetic Counselors (NSGC) recommend that patients' ancestry be obtained when taking a family history. However, no study has explored how consistently genetic counselors obtain or utilize this information. The goals of this study included assessing how genetic counselors collect their patients' ancestry, what factors influence this decision, and how they view the utility of this information. Genetic counselors working in a direct patient care setting in the US or Canada were recruited to participate in an anonymous survey via an NSGC email blast. Most participants (n = 115) obtain information about their patients' ancestry (96.5%), with the most common methods being directly asking the patient (91%) and utilizing intake forms (43.2%). Of participants who ask about ancestry directly, 50.5% always ask about the presence of Ashkenazi Jewish ancestry and 70.3% always ask about additional ancestries, suggesting that for most genetic counselors' collection of ancestry is standard practice. However, the clinical utility of ancestry information is highly variable, with the impact on genetic testing choice being particularly low. A slight majority of participants support a reevaluation of current ancestry guidelines (51.3%), with many participants suggesting that the varying utility of ancestry in different clinical indications/specialties should be incorporated into guidelines. Despite being standard practice for most genetic counselors, no unified approach or standard for how ancestral information should be used in genetic counseling practice was identified.
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 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 | 0.036 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".