Understanding psychiatrist perceptions surrounding psychiatric genetics and genetic counseling services
Bibliographic record
Abstract
The first specialty psychiatric genetic counselling (PGC) service began in Vancouver, \nCanada in 2012. Shortly thereafter, a genetic counselor in San Francisco, CA started a \nprivate PGC practice. Clear benefits of PGC have been demonstrated, including \nincreases in empowerment and self-efficacy among individuals with mental illness. \nDespite the availability and benefits of PGC, the majority of physicians are not \nreferring patients to the private PGC practice in San Francisco. Until now, no \nliterature has focused on psychiatrist perceptions of PGC services. This qualitative \nstudy examined the perceptions and beliefs of psychiatrists on the potential \nchallenges and benefits of PGC services for individuals with mental illness. Semistructured \ntelephone interviews were used to explore the experiences and perceptions \nof ten psychiatrists about psychiatric genetics and the potential clinical utility of PGC. \nAnalysis of interview transcripts revealed themes related to psychiatrists: 1) \nperceiving PGC as a potentially beneficial service in the future, but with significant \nlimitations in the present; 2) requiring more information about PGC above and \nbeyond current marketing methods; and 3) giving limited priority to discussing and \narranging PGC referrals because they (the psychiatrists) feel they already provide \ngenetic counseling to their patients. Identifying both conceptual and practical barriers \nto PGC services provides guidance for development of strategies to overcome these \nbarriers in the growing field of PGC services around the world.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".