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Record W2038316166 · doi:10.3109/17549507.2011.638727

Use of simulated patients for a student learning experience on managing difficult patient behaviour in speech-language pathology contexts

2012· article· en· W2038316166 on OpenAlexaff
Tim Bressmann, Alice Eriks‐Brophy

Bibliographic record

VenueInternational Journal of Speech-Language Pathology · 2012
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyInclusion (mineral)Interpersonal communicationLanguage Experience ApproachLanguage acquisitionMedical educationMathematics educationMedicineLanguage educationSocial psychology

Abstract

fetched live from OpenAlex

A student learning experience about managing difficult patients in speech-language pathology is described. In 2006, 40 students participated in a daylong learning experience. The first part of the experience consisted of presentations and discussions of different scenarios of interpersonal difficulty. The theoretical introduction was followed by an active learning experience with simulated patients. A similar experience without the simulated patients was conducted for 45 students in 2010. Both years of students rated the experience with an overall grade and gave qualitative feedback. There was no significant difference between the overall grades given by the students in 2006 and 2010. The qualitative feedback indicated that the students valued the experience and that they felt it added to their learning and professional development. The students in 2006 also provided detailed feedback on the simulation activities. Students endorsed the experience and recommended that the learning experience be repeated for future students. However, the students in 2006 also commented that they had felt inadequately prepared for interacting with the simulated patients. A learning experience with simulated patients can add to students' learning. The inclusion of simulated patients can provide a different, but not automatically better, learning experience.

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 score0.053
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.391
Teacher spread0.362 · 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

Citations26
Published2012
Admission routes1
Has abstractyes

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