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Record W1502131085 · doi:10.46743/2160-3715/2007.1640

Action Research as a Qualitative Research Approach in Inter- Professional Education: The QUIPPED Approach

2015· article· en· W1502131085 on OpenAlexaffabout
Margo Paterson, Jennifer Medves, Christine Chapman, Sarita Verma, Teresa Broers, Cori Schroder

Bibliographic record

VenueThe Qualitative Report · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of TorontoQueen's University
Fundersnot available
KeywordsCredibilityAction researchHealth careAction (physics)Public relationsSociologyPromotion (chess)Government (linguistics)Qualitative researchProfessional developmentParticipatory action researchPedagogyInterprofessional educationMedical educationEngineering ethicsMedicinePolitical scienceEngineeringSocial sciencePolitics

Abstract

fetched live from OpenAlex

The Canadian government supports the transformation of education for health care providers based on the recognized need for an inter- professional collaborative approach to care . This first paper in a series of papers demonstrates the credibility of an action research approach for the promotion and understanding of inter- professional education (IPE). Located in the critical paradigm, this action research project is concerned with creating an educational environment that enhances the ability of learners and educators to provide patient-centred care through inter- professional collaboration. The QUIPPED project has invited various stakeholders (faculty an d learners from various disciplines, consumers of health care, university administration and clinicians) to participate in the collaborative transformation of the educational culture and the co- creation of a shared knowledge for IPE.

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)0.240
metaresearch head score (Gemma)0.109
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.240
Threshold uncertainty score0.937

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2400.109
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0100.007
Science and technology studies0.0130.061
Scholarly communication0.0210.010
Open science0.0070.017
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.723
GPT teacher head0.762
Teacher spread0.038 · 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.

Study designQualitative
Domainnot available
GenreMethods

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
Published2015
Admission routes2
Has abstractyes

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