The impact of a “Psychiatric Genetics for Genetic Counselors” workshop on genetic counselor attendees: An exploratory study
Bibliographic record
Abstract
Genetic counseling is the process of supporting patients' and families' adaptation to genetic information. Psychiatric genetic counseling has been proven to be effective in improving empowerment, self-efficacy, and knowledge even in the absence of genetic testing. Despite this, only one specialist psychiatric genetic counseling clinic currently exists. In order to engage genetic counselors in providing psychiatric genetic counseling, a 2-day workshop: "Psychiatric Genetic Counseling for Genetic Counselors", was developed and implemented aimed at empowering genetic counselors to feel confident and competent in this practice domain. The aim of the study was to qualitatively explore the impact of the workshop. Semistructured interviews were carried out with 12 genetic counselors who attended the workshop between 2015 and 2018. Thematic analysis revealed that the workshop empowered all participants to feel comfortable and confident offering psychiatric genetic counseling to patients. Participants also reflected how the workshop highlighted the stigma associated with mental illnesses and offered support in normalizing these conditions. Overall, this study presents that the "Psychiatric Genetic Counseling for Genetic Counselors" workshop fulfilled its proposed aims and outcomes.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".