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Record W2004129863 · doi:10.1197/j.aem.2007.06.012

Public Health Considerations in Knowledge Translation in the Emergency Department

2007· article· en· W2004129863 on OpenAlexaff
Steven L. Bernstein, Edward Bernstein, Edwin D. Boudreaux, Charlene Babcock-Irvin, Michael J. Mello, Akhil Kapur, Bruce M. Becker, R. Sattin, V. Cohen, Gail D’Onofrio

Bibliographic record

VenueAcademic Emergency Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicinePsychological interventionPublic healthKnowledge translationHealth carePopulationPublic relationsMedical educationNursingKnowledge managementEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Effective preventive and screening interventions have not been widely adopted in emergency departments (EDs). Barriers to knowledge translation of these initiatives include lack of knowledge of current evidence, perceived lack of efficacy, and resource availability. To address this challenge, the Academic Emergency Medicine 2007 Consensus Conference, "Knowledge Translation in Emergency Medicine: Establishing a Research Agenda and Guide Map for Evidence Uptake," convened a public health focus group. The question this group addressed was "What are the unique contextual elements that need to be addressed to bring proven preventive and other public health initiatives into the ED setting?" Public health experts communicated via the Internet beforehand and at a breakout session during the conference to reach consensus on this topic, using published evidence and expert opinion. Recommendations include 1) to integrate proven public health interventions into the emergency medicine core curriculum, 2) to configure clinical information systems to facilitate public health interventions, and 3) to use ancillary ED personnel to enhance delivery of public health interventions and to obtain successful funding for these initiatives. Because additional research in this area is needed, a research agenda for this important topic was also developed. The ED provides medical care to a unique population, many with increased needs for preventive care. Because these individuals may have limited access to screening and preventive interventions, wider adoption of these initiatives may improve the health of this vulnerable population.

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)0.005
metaresearch head score (Gemma)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.253
GPT teacher head0.435
Teacher spread0.182 · 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

Citations37
Published2007
Admission routes1
Has abstractyes

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