The genomic era and perceptions of psychotic disorders: Genetic risk estimation, associations with reproductive decisions and views about predictive testing
Bibliographic record
Abstract
As a result of publicity surrounding genetic advances, increasing public awareness of a genetic role in major mental illness may be contributing to a "geneticization" of these illnesses. Geneticization could lead to oversimplified ideas about genetic risk, producing significant social consequences. We sought to investigate perceptions of genetic risk, associated effects on reproductive decisions and attitudes towards genetic testing amongst unaffected relatives of individuals with psychosis. A web-based survey design was used, which all visitors to a psychosis support/information website had the option to complete. Responders were representative of website visitors, and the study design facilitated collection of a large dataset, although the response rate was low. Over-estimating risk was associated with reproductive decisions favoring fewer children, and more positive attitudes towards genetic testing. Facilitating accurate risk perception through genetic counseling could significantly impact reproductive decisions, and the appropriate use of genetic tests in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".