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Record W3136551775 · doi:10.22230/jripe.2021v11n1a309

Preparedness for Interprofessional Learning: An Exploratory Study Among Health, Social Care, and Teacher Education Programs

2021· article· en· W3136551775 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
KeywordsPreparednessExploratory researchInterprofessional educationMedical educationHealth carePsychologyMedicineNursingSociologyPolitical science

Abstract

fetched live from OpenAlex

Background: Interprofessional learning (IPL) research is mainly restricted to health students. The purpose of this study was to assess the IPL preparedness of students from health, social care, and teacher education programs.Methods and findings: This project comprised an exploratory cross-sectionalstudy and online questionnaire. Of the 221 students included, the majority hadlearned about their own future role. In contrast, less than 20 percent had learnedabout other roles.Conclusions: This study suggests that teacher education and health and social carestudents were not equally prepared for IPL. Future research should explore howeducators may balance an unequal understanding of roles among students.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0050.002
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.143
GPT teacher head0.596
Teacher spread0.454 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations23
Published2021
Admission routes1
Has abstractyes

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