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Record W2783622286 · doi:10.22230/jripe.2017v7n1a262

An Autoethnographic Study of Interprofessional Education Partnerships

2018· article· en· W2783622286 on OpenAlexvenueno aff
Samantha Hurst, Karen Macauley, Linda Awdishu, Kathleen Sweeney, Sophie S Hutchins, Jennifer Namba, Michelle L. Johnson, Peggy A Wallace, Karen Garman, Amy Zheng

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2018
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsInterprofessional educationGeneral partnershipTeamworkMedical educationNarrativeAutoethnographyQualitative researchScale (ratio)PedagogyPsychologySociologyMedicineHealth careManagementPolitical science

Abstract

fetched live from OpenAlex

Background: Thiis qualitative longitudinal study describes an Interprofessional Education (IPE) collaboration between a public university with medical and pharmacy schools and a private, non-affiliated university with a nursing school. The study explores the dynamics of the IPE partnership and lessons learned over a three-year period in which members of the collaborative directed three IPE simulations.Methods and Findings: An autoethnographic inquiry technique was used to interview eight collaborators who designed and implemented a large-scale IPE simulation for approximately 300 students and 100 faculty members annually for three years. Two, 90-minute group narrative interviews were conducted and audio recorded for transcription and analysis. Five themes emerged: Natural Collaboration, Shared Vision and Commitment, Integrations and Synergy, All Hands on Deck, and Lasting Foundations. Collaborators agreed the joint effort was a positive experience with multidimensional returns on investment. They applied teamwork competencies to build the partnership, develop the IPE simulation, and overcome implementation challenges.Conclusions: Thiis article provides readers with the opportunity to learn from those who have been intimately involved in the design and implementation of a large-scale IPE collaboration to enhance the shared learning process for health students and faculty. Findings highlight the complexity of building an IPE collaborative and the necessity to build partnerships with facilitators committed to communication.

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)0.010
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 score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.003
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 head0.201
GPT teacher head0.630
Teacher spread0.429 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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