Genetic counseling students' use of patient‐centered communication skills predicts standardized patient satisfaction during virtual simulated sessions
Bibliographic record
Abstract
Abstract Communication is essential to effective genetic counseling, but few studies have systematically evaluated methods of assessing communication skills among genetic counseling trainees. The study's objective is to compare the strength of associations between standardized patient (SP) satisfaction with simulated genetic counseling sessions and student skill use during the sessions, as reported by SPs and students. We hypothesized that (1) Both SP‐ and student‐reported skill use will be significantly associated with SP satisfaction ratings during the baseline simulation and (2): SP ratings of student skill use will show a stronger relationship to SP satisfaction than student self‐rating of skill use. Sixty genetic counseling students and recent graduates (referred to as “students”) from accredited U.S. and Canadian programs participated in the study and completed a baseline virtual‐simulated genetic counseling session. Both students and SPs completed post‐session questionnaires about communication skill use (a 22‐item checklist) and SPs completed a satisfaction questionnaire based on the session (a 14‐item Likert scale). Multilevel regression models assessed associations between SP satisfaction during the baseline session and SP‐ or student‐reported skill use. SP satisfaction was significantly associated with skill use reported by both SPs and students, but the model based on SP report explained a higher proportion of the variance in SP satisfaction than student‐reported skill use (SP model fixed effects R2 = 27%, adjusted R2 = 21%; vs. student model R2 = 7%, adjusted R2 = −2%). For both the SP and student models, use of more skills from the LISTEN domain (which focused on eliciting the patient's perspective) was associated with higher SP satisfaction, while other skill category domains were not. These findings support the SP satisfaction measure as sensitive to variation in student performance of key communication skills, especially those eliciting the patient's perspective. Moreover, SP assessment of session satisfaction can be a useful assessment of student communication performance and a meaningful proxy for actual patient satisfaction.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".