Exploring genetic counselors' perspectives on family group appointments for genetic testing
Bibliographic record
Abstract
Increasing demand for genetics services has highlighted a need for more efficient genetic counseling service delivery models. Family group appointments (FGAs) may be a time-efficient approach to facilitate cascade genetic testing. This study aimed to explore genetic counselors' (GCs) perspectives on genetic counseling in a family group setting to inform effective practices for this service delivery method. Semi-structured interviews were conducted with GCs practicing in North America who have conducted FGAs for cascade genetic testing. Participants completed a survey about demographic information and FGA experience. A subset of survey respondents was invited to complete an interview. Interview transcripts were coded using an interpretive description approach. 138 GCs completed the survey and 13 participated in interviews. Genetic counselors reported that the benefits of FGAs include family member support, group discussion, efficiency, effects on cascade testing uptake, and job satisfaction. Key considerations included various logistical factors, respecting individual preferences about genetic testing, maintaining privacy, and existing family dynamics. Participants discussed how FGAs condense information and open appointment slots for other patients in their clinic. Logistical considerations identified were related to scheduling and attendance, such as GCs holding licensure in every state that patients are located in during the appointment, aligning multiple patient schedules, and flexibility with different appointment modalities. Benefits identified in our study highlight potential reasons that GCs and patients may consider FGAs as a preferred service delivery model for certain appointments. The considerations described in our study can help guide GCs arranging, conducting, and following up with a FGA. Our results also detail the importance of relational autonomy in medical decision-making, which is emphasized in FGAs and should be considered by genetics service providers accordingly.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".