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Record W2081575807 · doi:10.1002/chp.20028

Improving the clarity of the interprofessional field: Implications for research and continuing interprofessional education

2009· article· en· W2081575807 on OpenAlexaff
Joanne Goldman, Merrick Zwarenstein, Onil Bhattacharyya, Scott Reeves

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2009
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science CentreSt. Michael's Hospital
Fundersnot available
KeywordsCLARITYInterprofessional educationPsychological interventionConfusionField (mathematics)Medical educationPsychologyMedicineEngineering ethicsHealth careNursingPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Significant investments are being made around the world to improve interprofessional collaboration, yet limits in our knowledge of this field restrict the ability of decision makers to base their decisions upon evidence. Clarity of the interprofessional field is blurred by a conceptual and semantic confusion that affects our understanding of key elements of education and practice activities, their interlinked relationship, and their effects on health or system outcomes. Systematic reviews of interprofessional education (IPE) and interprofessional collaboration (IPC) have provided some insight into the nature and effectiveness of this field, but a lack of clarity remains. In this article we report on a scoping review currently being undertaken to analyze the interprofessional field, improve its conceptual clarity, and identify elements needed to enhance its development. Emerging review findings regarding participants and settings, interventions, and outcomes are reported. The article provides implications from this review and discusses them in relation to continuing IPE and future research.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.064
GPT teacher head0.534
Teacher spread0.471 · 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

Citations68
Published2009
Admission routes1
Has abstractyes

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