Psychiatrists' perceptions of and reactions to a simulated psychiatric genetic counseling session
Bibliographic record
Abstract
Psychiatric genetic counseling (pGC) has been demonstrated to have meaningful positive outcomes for people with psychiatric conditions and their families. However, it is not widely accessed, and clinical genetics services tend to receive few referrals for these indications. Little research has evaluated psychiatrists' perceptions of and experience with interfacing with pGC. Therefore, we invited Ontario-based psychiatrists to participate in a study in which they first watched a simulated pGC session (representative of typical practice: the patient had depression with no exceptionally dense family history of psychiatric conditions, no genetic testing is provided, and no family-based risk assessment is performed), then completed zoom-based qualitative semi-structured interviews. Interviews were recorded, transcribed verbatim and checked for accuracy. Using interpretive description to analyze interviews with 12 psychiatrists (data collection was stopped at this point, as theoretical sufficiency was achieved), we generated two theoretical models: the first described the decision-making pathway psychiatrists currently follow when determining whether and how to address genetics with a patient; the second described psychiatrists' ideas for integrating pGC into care models for the future. Our data shed light on how to facilitate the delivery of pGC for people with psychiatric conditions and their families.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".