The Efficacy of Genetic Counseling for Psychiatric Disorders: a Meta‐Analysis
Bibliographic record
Abstract
Psychiatric illnesses are complex, highly heritable disorders that have substantial implications for both affected individuals and their families. Though genetic testing is currently limited in its clinical usefulness in this area, interest in genetic counseling for psychiatric disorders has a relatively long history and many positive outcomes have been posited. Yet, empirical studies of genetic counseling outcomes have been emerging only more recently. The aim of the current meta-analysis was to analyze the efficacy of genetic counseling and explore potential moderators of its effect. An extensive electronic search was conducted investigating the literature published until July 2016. The initial search resulted in 2367 articles, four of which met the inclusion criteria and were included in the quantitative meta-analysis. Effect size parameters and sample sizes for all variables in each study were included. The efficacy has ben demonstrated both at post-intervention and at follow up, with an overall statistically significant effect size of moderate intensity. Implications of this study are discussed in detail.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.032 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".