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Record W2062689150 · doi:10.1197/j.aem.2007.06.025

Cognitive and Social Issues in Emergency Medicine Knowledge Translation: A Research Agenda

2007· article· en· W2062689150 on OpenAlexaff
Jamie Brehaut, Robert M. Hamm, Sumit R. Majumdar, Frank J. Papa, Anthony Lott, Eddy Lang

Bibliographic record

VenueAcademic Emergency Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsMcGill UniversityUniversity of AlbertaUniversity of Ottawa
Fundersnot available
KeywordsKnowledge translationMedicineCLARITYPsychological interventionCognitionRelevance (law)Social cognitive theoryAutonomyProcess (computing)Emergency departmentMedical educationKnowledge managementPsychologyNursingSocial psychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

The individual practitioner is a linchpin in the process of translating new knowledge into practice, particularly in the emergency department, where physician autonomy is high, resources are limited, and decision-making situations are complex. An understanding of the cognitive and social processes that affect knowledge translation (KT) in emergency medicine (EM) is crucial and at present understudied. As part of the 2007 Academic Emergency Medicine Consensus Conference on KT in EM, our group sought to identify key research areas that would inform our understanding of these cognitive and social processes. We combined an online discussion group of interdisciplinary stakeholders, an extensive review of the existing literature, and a "public hearing" of the recommendations at the Consensus Conference to establish relative preference for the recommendations, as well as their relevance and clarity to attendees. We identified five key research areas as follows. 1) What provider-specific barriers/facilitators to the use of new knowledge are relevant in the EM setting? 2) Can social psychological theories of behavior change be used to develop better KT interventions for EM? 3) Can the study of "distributed cognition" suggest new vehicles for KT in the emergency department? 4) Can the concept of dual-process reasoning inform our understanding of the KT process? 5) Can patient-specific, immediate feedback serve as a vehicle for KT in EM? We believe that exploring these key research questions will directly lead to improved KT interventions and to further discussion of the cognitive and social factors impacting KT in EM.

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)0.009
metaresearch head score (Gemma)0.046
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), 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 score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.306
GPT teacher head0.550
Teacher spread0.244 · 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

Citations26
Published2007
Admission routes1
Has abstractyes

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