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Record W2103716505 · doi:10.3109/13561820.2011.640758

A cross-institutional examination of readiness for interprofessional learning

2012· article· en· W2103716505 on OpenAlexaff
Sharla King, Elaine Greidanus, Rochelle Major, Tatiana LoVerso, Alan Knowles, Mike Carbonaro, Louise M. Bahry

Bibliographic record

VenueJournal of Interprofessional Care · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMacEwan UniversityUniversity of Alberta
Fundersnot available
KeywordsConfirmatory factor analysisInterprofessional educationCurriculumMedical educationPsychologyGovernment (linguistics)Scale (ratio)Variance (accounting)PedagogyPolitical scienceMedicineHealth careStructural equation modelingBusinessComputer science

Abstract

fetched live from OpenAlex

This paper examines the readiness for and attitudes toward interprofessional (IP) education in students across four diverse educational institutions with different educational mandates. The four educational institutions (research-intensive university, baccalaureate, polytechnical institute and community college) partnered to develop, deliver and evaluate IP modules in simulation learning environments. As one of the first steps in planning, the Readiness for Interprofessional Learning Scale was delivered to 1530 students from across the institutions. A confirmatory factor analysis was used to expand upon previous work to examine psychometric properties of the instrument. An analysis of variance revealed significant differences among the institutions; however, a closer examination of the means demonstrated little variability. In an environment where collaboration and development of learning experiences across educational institutions is an expectation of the provincial government, an understanding of differences among a cohort of students is critical. This study reveals nonmeaningful significant differences, indicating different institutional educational mandates are unlikely to be an obstacle in the development of cross-institutional IP curricula.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.103
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.475
Teacher spread0.438 · 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

Citations26
Published2012
Admission routes1
Has abstractyes

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