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Record W2082707533 · doi:10.3109/13561820.2014.944259

Interprofessional education using simulation of an overnight inpatient ward shift

2014· article· en· W2082707533 on OpenAlexaff
Kristina M. Joyal, Cara Katz, Nicole Harder, Heather Dean

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInterprofessional educationPharmacyHealth professionsMedical educationHealth carePsychologyEvent (particle physics)NursingNightmareMedicine

Abstract

fetched live from OpenAlex

In healthcare it is rare for professionals to practice together before they practice together. Nightmare Night Care is an annual interprofessional voluntary event for health sciences students in nursing, medicine and pharmacy to come together for a simulated hospital overnight ward shift. The purpose of this study was to investigate the interprofessional knowledge, skills and attitudes the students learn from this experience. Students responded to surveys before (n = 45) and after the event (n = 11) regarding their understanding of the goals of interprofessional education (IPE), roles and responsibilities of other professions, and what they learned from this event. Responses demonstrated that students are eager to learn in interprofessional settings and that IPE events may aid in building understanding and communication between professions. IPE events are an opportunity to allow students to learn about each other; however, they must occur frequently, and must include an orientation on role clarification if they are to have an effect on changing preconceived stereotypes of the other professions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.462
Teacher spread0.437 · 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.

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

Citations32
Published2014
Admission routes1
Has abstractyes

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