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Record W1985248569 · doi:10.3109/13561820.2012.736889

Theories, relationships and interprofessionalism: Learning to weave

2012· article· en· W1985248569 on OpenAlexaff
Pippa Hall, Lynda Weaver, Pamela Grassau

Bibliographic record

VenueJournal of Interprofessional Care · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of OttawaBruyère
Fundersnot available
KeywordsToolboxInterprofessional educationValue (mathematics)PsychologyDominance (genetics)Health careSociologyComputer science

Abstract

fetched live from OpenAlex

In this article, we illustrate the application of a number of theoretical frameworks we have used to guide our work in interprofessional education (IPE) and collaborative interprofessional care (IPC). Although we do not claim to be experts in any one of these theories, each has offered important insights that have broadened our understanding of the complexities of interprofessional learning and practice. We have gained an appreciation for an increasing number of theories relevant to IPE and IPC, and, as a result, we have woven together more key principles from different theories to develop activities for all levels of interprofessional learners and clinicians. We pay particular attention to relational competencies, knotworking/idea dominance, targeted tension and situational awareness. We are now drawing on the arts and humanities and complexity theory to foster relationship-building learning. Evaluation of our endeavors will eventually follow these latter theories for methods that better match the human and social experiences that underpin learning. Our "theoretical toolbox" therefore may be of value to educators who develop and implement creative interprofessional learning activities, as well as clinicians interested in moving toward more effective collaboration.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.032
GPT teacher head0.432
Teacher spread0.400 · 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 designObservational
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

Citations33
Published2012
Admission routes1
Has abstractyes

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