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Record W2093403235 · doi:10.12927/hcq.2013.22381

Strategic Approach to Building Research Capacity in Inter-professional Education and Collaboration

2011· article· en· W2093403235 on OpenAlexaffabout
Esther Suter, Jana Lait, Laura MacDonald, Pamela Wener, Rebecca M. Law, Hossein Khalili, Patricia McCarthy

Bibliographic record

VenueHealthcare Quarterly · 2011
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsGlobeHealthcare deliveryBest practiceCapacity buildingHealth careBusinessPublic relationsProfessional developmentHealth professionalsKnowledge managementMedical educationMedicinePolitical scienceManagementComputer scienceEconomics

Abstract

fetched live from OpenAlex

The purpose of this paper is to describe the process used to initiate research capacity building in a community of practice (CoP) focused on the research and evaluation of inter-professional education and collaboration. This CoP, composed of members from across Canada, is a committee of the Canadian Interprofessional Health Collaborative (CIHC), a national collaborative that aims to advance inter-professional education and collaboration in healthcare. The committee mapped recommendations that emerged from a number of CIHC reports onto a research capacity building framework. The expertise of the diverse members in conjunction with this unique mapping process allowed the committee to identify its long-term research and evaluation objectives and strategies. This resulted in the formation of three working groups, each tasked with activities that contribute to the committee's overall goal of building research capacity in inter-professional education and collaboration. A framework provides a structured approach to identifying research and evaluation priorities and objectives. Furthermore, the process of applying the framework engages the committee members in determining the course of action. The process can be easily transferred to other areas in need of research capacity building.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.459
GPT teacher head0.516
Teacher spread0.057 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations7
Published2011
Admission routes2
Has abstractyes

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