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Record W3136114582 · doi:10.22230/jripe.2021v11n1a319

Large-scale Blended Learning Design in an Undergraduate Interprofessional Course in Norway: Students’ Perspectives from an Exploratory Study

2021· article· en· W3136114582 on OpenAlexvenueno aff
Kari Almendingen, Marianne Molin, Jūratė Šaltytė Benth

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsBlended learningInterprofessional educationMedical educationExploratory researchPsychologyScale (ratio)Health careMathematics educationMedicinePedagogyEducational technologySociology

Abstract

fetched live from OpenAlex

Background: The purpose of this study was to assess learning outcomes and student satisfaction after participating in a large-scale interprofessional (IPL) blended learning course.Methods and findings: In this cross-sectional study, students from health, social care, and teacher education programs completed two questionnaires. The majority were satisfied with the blended learning approach. The IPL group discussions resulted in learning outcomes that were two times higher than those from traditional instruction, including lectures and assignments. Health and social care students reported lower learning outcomes and satisfaction than teacher education and child welfare students (p < 0.05).Conclusions: The study demonstrated the feasibility of the blended learningapproach. However, IPL activities that are explicitly inclusive for all studentsshould be created for future courses.

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)0.014
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.006
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 head0.101
GPT teacher head0.571
Teacher spread0.471 · 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 designQualitative
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

Citations12
Published2021
Admission routes1
Has abstractyes

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