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Record W2076900326 · doi:10.1197/j.aem.2007.06.033

Development of the Capacity Necessary to Perform and Promote Knowledge Translation Research in Emergency Medicine

2007· article· en· W2076900326 on OpenAlexaff
Peter S. Dayan, Martin H. Osmond, Nathan Kuppermann, Eddy Lang, Terry P. Klassen, David W. Johnson, Stefan Strauß, Erik P. Hess, Sandra M. Schneider, Marc Afilalo, Martin Pusic

Bibliographic record

VenueAcademic Emergency Medicine · 2007
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineKnowledge translationProcess (computing)Field (mathematics)Research developmentSet (abstract data type)Translational researchKnowledge managementComputer sciencePathology

Abstract

fetched live from OpenAlex

Knowledge translation (KT) research in emergency medicine (EM) is in its infancy, and few EM investigators have the skills needed to perform KT research. Furthermore, the capacity to perform such KT research is underdeveloped in the field of EM. This consensus group used an iterative process to set forth initial recommendations and suggest methods for the development of EM KT research capacity. We have emphasized the need to form sustainable linkages, particularly between EM researchers and KT scientists, and to educate EM researchers in KT research methods to help create and sustain a culture of KT in our field. EM KT researchers must also engage local and national organizations and stakeholders to fund and promote KT research. Finally, we see the need to further develop and support EM research networks, as these networks will be both the clinical laboratories in which to perform the KT research and the incubators for the development of EM KT research experts.

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)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.447
GPT teacher head0.570
Teacher spread0.124 · 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

Citations19
Published2007
Admission routes1
Has abstractyes

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