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Record W2902261948 · doi:10.30707/tlcsd2.2towson

Impact of Virtual Simulation and Coaching on the Interpersonal Collaborative Communication Skills of Speech-Language Pathology Students: a Pilot Study

2018· article· en· W2902261948 on OpenAlexfundno aff
CCC-SLP Jacqueline A. Towson, Matthew Taylor, Jennifer Tucker, B Paul, Patrick Pabian, CCC-SLP Richard I. Zraick

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
FundersToronto Rehabilitation Institute
KeywordsCoachingInterpersonal communicationPsychologyMedical educationAvatarIntervention (counseling)Social skillsNonverbal communicationComputer-mediated communicationTask (project management)Applied psychologyMedicineSocial psychologyComputer scienceDevelopmental psychologyThe Internet

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.449
Teacher spread0.410 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2018
Admission routes1
Has abstractyes

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