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Record W2141337439 · doi:10.22230/jripe.2014v3n3a132

Interprofessional Collaboration Led by Health Professional Students: A Case Study of the Inter Health Professional Alliance at Virginia Commonwealth University

2014· article· en· W2141337439 on OpenAlexvenueno aff
Lynn M. VanderWielen, K. Elizabeth, Hadja I. Diallo, Kristen N. LaCoe, Natalie Nguyễn, Sonal A. Parikh, Helen Y. Rho, Alexander S. Enurah, Erika K. Dumke, Alan Dow

Bibliographic record

VenueJournal of Research in Interprofessional Practice and Education · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsAllianceCommonwealthEnthusiasmCLARITYHealth careMedical educationCore competencyCompetence (human resources)Participatory action researchInterprofessional educationMedicineCitizen journalismCall to actionProfessional developmentNursingPsychologyPolitical scienceSociologyManagement

Abstract

fetched live from OpenAlex

Background: Internationally recognized health experts have identified the need for an interdisciplinary approach to meet the healthcare needs of the 21st century, but academic institutions have been slow to take action. In response, eight health professional students at Virginia Commonwealth University developed a student-led organization, the Inter Health Professionals Alliance (IHPA), to foster a collaborative, interdisciplinary environment among health professional students.Methods and Findings: The eight students utilized a participatory action research approach to identify 1) an understanding behind the motivation for developing IHPA and 2) the core benefits of group involvement. Four benefits were identified: the development of knowledge and skills, interprofessional networks, professional competence, and role clarity. The case study demonstrated that students can engage in interdisciplinary collaboration from a student-initiated approach and likely improve the care of future patients. Drawing on personal experiences, IHPA board members outline five pieces of wisdom to aid fellow students in the development of student-led interdisciplinary organizations.Conclusions: With enthusiasm and support, students can transform their educational experiences to meet the healthcare needs of the twenty-first century.

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)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.560
Teacher spread0.509 · 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
Published2014
Admission routes1
Has abstractyes

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