Factors associated with <scp>US</scp> and Canadian genetic counselors' testing decisions during pregnancy
Bibliographic record
Abstract
Decision-making regarding prenatal screening and diagnostic testing has become more complex as the number of options has increased, with pregnant patients having access to more information about their pregnancies than ever before. Genetic counselors have extensive training in prenatal genetic screening and testing options, but personal decision-making in this well-informed population remains largely unstudied. This study describes the prenatal testing decisions genetic counselors made during their own pregnancies, and the factors identified as important when making those decisions. A web-based, mixed-methods survey was distributed to members of multiple professional societies for genetic counselors. A total of 318 genetic counselors across numerous specialties in the United States and Canada participated in this study. The satisfaction with decision scale was modified and applied to measure participants' decisional satisfaction. In their most recent pregnancies, most genetic counselors pursued carrier screening (77%) and aneuploidy and/or open neural tube defect screening (88%). A minority of genetic counselors (15%) utilized diagnostic testing. Common factors considered when making testing decisions included wanting information that could impact future decisions, test specifics (e.g., accuracy, methodology, and content), and knowledge gained from participants' genetic counseling background. The uptake of diagnostic testing among prenatal genetic counselors was significantly greater (p < 0.05) than the uptake among genetic counselors in other specialties. This informed study population largely self-directed their own prenatal care, leading to high satisfaction with their decisions. Data in this study provide evidence for promoting participation in prenatal screening and testing decision-making to maximize decisional satisfaction.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".