The effectiveness of psychiatric genetic counseling training: An analysis of 13 international workshops
Bibliographic record
Abstract
Studies have consistently shown that psychiatric genetic counseling (pGC) helps people with psychiatric conditions by increasing empowerment and self-efficacy, and addressing emotions like guilt. Yet, it is not routinely provided. Genetic counselors and trainees express low confidence in their ability to provide meaningful pGC, especially in the absence of adequate training. Therefore, to address this gap a "Psychiatric Genetic Counseling for Genetic Counselors" (PG4GC) workshop was developed and delivered to 13 groups of participants (primarily qualified genetic counselors and trainees) between 2015 and 2023 (10 workshops were delivered in-person, and three virtually). Participants completed quantitative questionnaires both before and after completing the workshop to assess their comfort, knowledge, behavior, and feeling of being equipped to provide pGC. In total, 232 individuals completed the pre-workshop questionnaire and 154 completed the post-workshop questionnaire. Participants felt more comfortable, knowledgeable, and equipped to provide pGC, and reported being more likely to address psychiatric concerns after the workshop, regardless of whether they were trainees or practicing professionals and whether they completed the workshop in-person or virtually. This study suggests that the PG4GC workshop is an effective educational tool in pGC training that may aid in broader implementation of the service.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".