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Record W2002654706 · doi:10.2147/amep.s12584

Interprofessional Resource Centre: a knowledge translation strategy

2011· article· en· W2002654706 on OpenAlexafffund
Christine Patterson, Julie Vohra, David Price, Gladys Peachey, Arthur Arthur, Rob Mariani, Paul Dymel, Kevin P. Timms, Ellis Westwood

Bibliographic record

VenueAdvances in Medical Education and Practice · 2011
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcMaster UniversityHeart and Stroke FoundationHamilton Health SciencesMcMaster Children's Hospital
FundersMcMaster University
KeywordsKnowledge translationResource (disambiguation)Translation (biology)Computer scienceData scienceKnowledge managementBiology

Abstract

fetched live from OpenAlex

The Interprofessional Resource Centre (IRC) was based on an extensive literature search and a provincial consultative process that involved administrators, health care providers, educators, preceptors, and alternative and complementary health care providers from different disciplines. Information from the literature review was synthesized into a logic model that served as a preliminary outline for the IRC to be further developed during the stakeholder consultation. The findings from the literature were triangulated with the opinions of different groups of key stakeholders who participated in three different methods of data collection: 1) a large-scale deliberative survey, 2) an in-person dialogue, and 3) targeted questionnaires. The result of this process was an online tool that presents information on what needs to be considered when planning interprofessional practice and education within an organization with the purpose of: 1) building capacity within agencies for interprofessional, collaborative practice; 2) providing preceptors with educational strategies to develop interprofessional competencies in their students; 3) promoting the use of technology as a strategy for knowledge transfer within the agencies and between educational institutions; and 4) developing an evaluation plan to measure interprofessional practice and education.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.072
GPT teacher head0.524
Teacher spread0.452 · 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 designNot applicable
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

Citations1
Published2011
Admission routes2
Has abstractyes

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