Research priorities in psychiatric genetic counselling: how to talk to children and adolescents about genetics and psychiatric disorders
Bibliographic record
Abstract
It is now well established that mental health disorders are heritable [ 1 ]. Genetic counselling is a process through which a trained professional helps an individual to better understand and adapt to the medical, psychological, and familial implications of genetic contributions to disease [ 2 ]. As of yet, no genetic tests to confirm a psychiatric diagnosis exist and alone they are unlikely to be sufficient for diagnosis. However, a formal genetic test is not required for the delivery of genetic counselling and for the benefits to be realised [ 3 ]. Psychiatric genetic counselling is conceptually identical to genetic counselling for other types of disorders and its efficacy for adults diagnosed with psychiatric disorders and their family members has been documented [ 4 ]. We are now entering an era in which genetic information is likely to become far more available as it is gradually integrated into healthcare. Thus, how to effectively communicate complex genetic risk information is of high research priority. Communicating genetic risk for psychiatric disorders comes with many challenges due to their complex and multifactorial nature. Nonetheless, psychiatric genetic counselling is associated with a number of positive immediate and long-term outcomes [ 4 ]. For instance, psychiatric genetic counselling can tackle misconceptions about causes of illness, address genetic and/or environmental determinism, empower and reduce shame and/or guilt, change one’s approach to treatment, and enable more informed decision-making regarding major life decisions, such as having children [ 4 ]. Such established benefits suggest that psychiatric genetic counselling will become an important part of clinical care for psychiatric patients in the future. Half of mental health disorders start before the age of 14 [ 5 ], with 1 in 7 young people between 10 to 19 years old experiencing mental ill health [ 6 ]. Thus, childhood or adolescence could be a particularly suitable window within which to receive psychiatric genetic counselling. This may prevent misconceptions about the causes of one’s mental illness, manage stigmatising beliefs related to personal or family history of mental health problems [ 7 ], and encourage risk-reducing behaviours [ 8 , 9 ]. Psychiatric genetic counselling could also have a positive impact on parents and caregivers, who often feel responsible for their child’s mental health and may experience feelings of guilt, shame, or a heavy burden of responsibility [ 10 ]. Such feelings may be partially rooted in a limited understanding of the contributions of genetic and environmental factors to mental disorders [ 11 ]. This can have a variety of negative behavioural consequences such as not seeking out suitable support for their child or potentially limiting the number of their future children [ 12 , 13 ].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".