Psychiatric genetic counseling: A survey of Australian genetic counselors' practice and attitudes
Bibliographic record
Abstract
Genetic counseling plays a critical role in supporting individuals and their families' adaption to psychiatric conditions, addressing the multifactorial nature of these conditions in a personally meaningful and empowering way. Yet data related to the practice and attitudes of Australian genetic counselors about psychiatric genetic counseling (PGC) is limited. This survey investigated the practice of Australian genetic counselors, and their attitudes toward PGC. Genetic counselors (N = 393) were invited to participate in an anonymous online survey between March and May 2022. Forty-four genetic counselors (response rate = 11%) from Australia and New Zealand responded. No respondents practice in psychiatric genetics as their speciality area; most respondents do not see any patients where the primary indication is a personal and/or family history of psychiatric disorders (91%). Greater than half of respondents (56%) believed there was sufficient evidence to support PGC, and 64% enquire about personal and/or family history of psychiatric disorders, but only 25% provide genetic counseling on this topic. Most respondents do not feel confident providing risk assessments for psychiatric disorders (72%), while the majority expressed interest in attending specialist training (95%), and in incorporating PGC into future practice (77%). Australian genetic counselors would benefit from psychiatric genetic education and training, and establishment of specialized PGC services would address this gap in patient care, while providing opportunities for genetic counselors to gain skills and experience in PGC.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".