Group genetic counseling: An alternate service delivery model in a high risk prenatal screening population
Bibliographic record
Abstract
OBJECTIVE: To address the growing demand for prenatal genetic services, group genetic counseling was explored as an alternative service delivery model for women with a positive prenatal screening result. METHOD: Women were recruited from a prenatal genetic service and systematically allocated to a traditional individual appointment with a genetic counselor or a group genetic counseling session. Questionnaires were administered to assess patient psychological outcomes, knowledge, and satisfaction following individual and group genetic counseling for a positive prenatal screen. Genetic counselor time per type of patient was measured. RESULTS: Of 172 participants, 107 (62.2%) received group genetic counseling and 65 (37.8%) received individual genetic counseling. Both group and individual genetic counseling encounters significantly decreased patient anxiety, increased perceived personal control, decreased decisional conflict, and increased knowledge. Satisfaction was high following both methods. Anxiety was significantly decreased in women who received individual genetic counseling compared with group sessions (P < .001). Genetic counselors spent less time per group patient seen compared with individual patients. CONCLUSION: Group genetic counseling followed by the option of brief individual genetic counseling appears acceptable to women in a high-risk prenatal screening population. The findings support an alternative service delivery model for prenatal genetic services that could optimize the utilization of genetic counseling resources.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".