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Record W2148827983 · doi:10.3109/13561820.2012.759911

Taking the long view: Exploring the development of interprofessional education

2013· article· en· W2148827983 on OpenAlexaff
Jan Fook, Lynda D’Avray, Caroline Norrie, Maria Psoinos, Bryony Lamb, Fiona Ross

Bibliographic record

VenueJournal of Interprofessional Care · 2013
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsDalhousie University
Fundersnot available
KeywordsInterprofessional educationGeneral partnershipContext (archaeology)Perspective (graphical)Health careDisciplinePublic relationsProfessional boundariesSociologyMedical educationPsychologyNursingMedicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

Interprofessional education (IPE) in health and social care has been well documented regarding student outcomes. Less has been written from the perspective of those who actually developed IPE. This study explores IPE within the context of a university partnership working with service providers in Southwest London (UK). We focused on the experiences and perspectives of 19 key players who were interviewed about the inception, implementation and development of IPE over 15 years. Our aim was to understand their views of IPE and its evolution over time. Interviewees provided different understandings of IPE, as well as contrasting views regarding its purpose and optimum delivery. Problems such as lack of central planning and the logistics of implementation were also discussed. Paradoxically, however, the participants highlighted positive outcomes and conveyed optimistic messages for the future. Despite various challenges and setbacks, a strong belief in the importance of IPE and a commitment to carrying it through were strong motivators contributing to finding solutions, as were building trust and positive relationships across professional and disciplinary boundaries.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, 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 score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.438
Teacher spread0.381 · 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

Citations27
Published2013
Admission routes1
Has abstractyes

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