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Record W2941727428 · doi:10.1177/2377960819835735

The Point of View of Undergraduate Health Students on Interprofessional Collaboration: A Thematic Analysis

2019· article· en· W2941727428 on OpenAlexaff
Monica Bianchi, Annamaria Bagnasco, Luca Ghirotto, Giuseppe Aleo, Gianluca Catania, Milko Zanini, Franco A. Carnevale, Loredana Sasso

Bibliographic record

VenueSAGE Open Nursing · 2019
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcGill University
Fundersnot available
KeywordsThematic analysisTUTORMedical educationInterprofessional educationPerceptionPsychologyQualitative researchResource (disambiguation)MedicinePedagogyHealth careComputer scienceSociology

Abstract

fetched live from OpenAlex

Interprofessional education (IPE) is essential to prepare future professionals for interprofessional collaboration (IPC). Learning together is essential for students because it is a way to understand the roles of other colleagues, improve their skills, knowledge, competencies, and attitudes to collaborate with the interprofessional teams. To explore how undergraduate students who attend IPE courses define IPC, a qualitative study using semistructured interviews followed by a thematic analysis was performed. Four main themes were identifed: IPC as a resource, requirements for IPC, emotions linked to IPC, and tutor's role to facilitate students' perception of IPC. Students considered IPE important to build IPC, where clinical placement tutors play a key role. The most important findings of the present study include the students' considerations about the importance of IPE when building their IPC definition and the key role played by the tutor during the placement in building IPC in clinical practice.

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)0.015
metaresearch head score (Gemma)0.020
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 score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0040.004
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0010.002
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 head0.032
GPT teacher head0.506
Teacher spread0.474 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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