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Record W2058338640 · doi:10.3109/17518423.2014.989461

Developing authentic clinical simulations for effective listening and communication in pediatric rehabilitation service delivery

2014· article· en· W2058338640 on OpenAlexaff
Gillian King, Tracy A. Shepherd, Michelle Servais, Colleen Willoughby, Linda Bolack, Deborah Strachan, Sheila Moodie, Patricia Baldwin, Kerry Knickle, Kathryn Parker, Diane Savage, Nancy McNaughton

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2014
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsThames Valley Children's CentreWestern UniversityHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
Fundersnot available
KeywordsActive listeningInterpersonal communicationProcess (computing)Computer scienceScale (ratio)Service delivery frameworkRehabilitationPsychologyService (business)MultimediaSocial psychologyCommunication

Abstract

fetched live from OpenAlex

PURPOSE: To describe the creation and validation of six simulations concerned with effective listening and interpersonal communication in pediatric rehabilitation. METHODS AND FINDINGS: The simulations involved clinicians from various disciplines, were based on clinical scenarios related to client issues, and reflected core aspects of listening/communication. Each simulation had a key learning objective, thus focusing clinicians on specific listening skills. The article outlines the process used to turn written scenarios into digital video simulations, including steps taken to establish content validity and authenticity, and to establish a series of videos based on the complexity of their learning objectives, given contextual factors and associated macrocognitive processes that influence the ability to listen. A complexity rating scale was developed and used to establish a gradient of easy/simple, intermediate, and hard/complex simulations. CONCLUSIONS: The development process exemplifies an evidence-based, integrated knowledge translation approach to the teaching and learning of listening and communication skills.

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)0.004
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 score0.106
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.036
GPT teacher head0.379
Teacher spread0.343 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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