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Record W2680720051 · doi:10.1007/s10897-017-0113-8

The Efficacy of Genetic Counseling for Psychiatric Disorders: a Meta‐Analysis

2017· review· en· W2680720051 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Genetic Counseling · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGenetic counselingMeta-analysisClinical psychologyPsychiatryMedicineIntervention (counseling)Public healthSample size determinationPsychologyGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.377
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it