Impact of Virtual Simulation and Coaching on the Interpersonal Collaborative Communication Skills of Speech-Language Pathology Students: a Pilot Study
Bibliographic record
Abstract
Communication between clinicians, teachers, and family members is a critical skill when addressing and providing for the individual needs of patients. However, graduate students in speech-language pathology (SLP) programs often have limited opportunities to practice these skills prior to or during externship placements. The purpose of this study was to explore the use of virtual-reality based rehearsal with coaching on the interpersonal collaborative communication skills of SLP graduate students when delivering information regarding a singular patient to different stakeholders. Three graduate students completing their third semester in a SLP program participated in the study. Each participant was provided a clinical case scenario and asked to deliver recommendations related to the client’s communication abilities to a single adult avatar portraying either a parent, teacher, or pediatrician. This task was repeated twice to allow assessment of performance across multiple trials. A brief reflection and coaching period was provided between trials with the same avatar. All interactions were scored using the Situation, Background, Assessment, Recommendation, and Communication (SBAR-C) tool. All participants demonstrated improved communication skills between their first and second trial with each avatar as measured by the SBAR-C. Social validity surveys with participants revealed that they found the intervention to be valid and acceptable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".