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Record W2548063845 · doi:10.22230/jripe.2017v6n2a225

Promoting Interprofessional Collaboration: A Pilot Project Using Simulation in the Virtual World of Second Life

2016· article· en· W2548063845 on OpenAlexvenueno aff
Deborah Davis, Gylo Hercelinskyj, Lynette M. Jackson

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2016
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsInterprofessional educationCurriculumMedical educationTeamworkHealth careVirtual worldHealth professionalsPsychologyMedicineComputer sciencePedagogyHuman–computer interactionPolitical science

Abstract

fetched live from OpenAlex

Background: Contemporary health services increasingly call for teamwork and interprofessional collaboration, though undergraduate curricula provide few opportunities for students to develop the necessary skills. This article presents the results of an innovative pilot project focusing on providing an interprofessional clinical learning experience for students using the virtual world of Second Life.Methods and Findings: A pilot project was implemented and tested on a small group of students studying at two institutions in four healthcare programs. Qualitative descriptive methods were employed to analyze semi-structured interview transcripts. The evaluation revealed that participants were easily able to manage the technologies associated with Second Life and the learning and teaching strategies were engaging and useful. While the project provided students with an opportunity to learn more about the role of other health professionals and their contribution to patient care, it will require some development before it achieves in full the aim to promote interprofessional collaboration. Conclusions: Simulation in virtual worlds such as Second Life offers promise in the area of interprofessional education.

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)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.191
GPT teacher head0.567
Teacher spread0.377 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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