Psychiatric genetic counselling for parents of individuals affected with psychotic disorders: a pilot study
Bibliographic record
Abstract
AIM: There has been increasing interest in the concept of applying genetic counselling to psychiatric disorders, but despite its relevance to psychiatric illness, and interest from the target group, there have been no empiric investigations of psychiatric genetic counselling. In a population of unaffected parents of individuals with first episode as well as more chronic psychotic disorders, we aimed to investigate whether psychiatric genetic counselling: is perceived to be useful, could increase understanding of the causes of psychiatric illness and decrease concern about other relatives becoming affected. METHODS: Subjects (n=13) participated in a genetic counselling session. The session was a clinical intervention similar to what would be carried out as part of a referral for any disease with a hereditary component, but specific for psychosis. Questionnaires were used to assess (pre-counselling): motivations for attending, concern about other relatives developing psychiatric illness, and (post-counselling) whether the intervention: (i) improved understanding of mental illness; (ii) modified concern about other relatives becoming affected; and (iii) was perceived to be useful. RESULTS: Desire for knowledge motivated participation. Immediately after the session, and 1 month later >92% and 100% of participants, respectively, felt that the session was useful. Genetic counselling reduced concern about other relatives becoming affected as risks were lower than participants had expected. All participants felt that their understanding of the causes of psychiatric illness had improved through genetic counselling. CONCLUSION: Psychiatric genetic counselling may benefit parents of individuals with psychiatric illnesses. Avenues for future research are highlighted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".