Evaluating Genetic Counseling for Family Members of Individuals With Schizophrenia in the Molecular Age
Bibliographic record
Abstract
BACKGROUND: Myths and concerns about the extent and meaning of genetic risk in schizophrenia may contribute to significant stigma and burden for families. Genetic counseling has long been proposed to be a potentially informative and therapeutic intervention for schizophrenia. Surprisingly, however, available data are limited. We evaluated a contemporary genetic counseling protocol for use in a community mental health-care setting by non-genetics professionals. METHODS: We used a pre-post study design with longitudinal follow-up to assess the impact of genetic counseling on family members of individuals with schizophrenia, where molecular testing had revealed no known clinically relevant genetic risk variant. We assessed the outcome using multiple measures, including standard items and scales used to evaluate genetic counseling for other complex diseases. RESULTS: Of the 122 family members approached, 78 (63.9%) actively expressed an interest in the study. Participants (n = 52) on average overestimated the risk of familial recurrence at baseline, and demonstrated a significant improvement in this estimate postintervention (P < .0001). This change was associated with an enduring decrease in concern about recurrence (P = .0003). Significant and lasting benefits were observed in other key areas, including increased knowledge (P < .0001) and a decreased sense of stigma (P = .0047). Endorsement of the need for genetic counseling was high (96.1%). CONCLUSIONS: These results provide initial evidence of the efficacy of schizophrenia genetic counseling for families, even in the absence of individually relevant genetic test results or professional genetics services. The findings support the integration of contemporary genetic counseling for families into the general management of schizophrenia in the community.
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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 | 0.006 | 0.012 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".