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Record W2338981011 · doi:10.1111/jpc.13074

Knowledge translation studies in paediatric emergency medicine: A systematic review of the literature

2016· review· en· W2338981011 on OpenAlexaff
Catherine Wilson, David W. Johnson, Ed Oakley

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2016
Typereview
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePsychological interventionKnowledge translationEmergency departmentIntervention (counseling)Clinical study designMEDLINESystematic reviewCluster (spacecraft)Clinical trialFamily medicinePediatricsNursingPathology

Abstract

fetched live from OpenAlex

AIM: Systematic review of knowledge translation studies focused on paediatric emergency care to describe and assess the interventions used in emergency department settings. METHODS: Electronic databases were searched for knowledge translation studies conducted in the emergency department that included the care of children. Two researchers independently reviewed the studies. RESULTS: From 1305 publications identified, 15 studies of varied design were included. Four were cluster-controlled trials, two patient-level randomised controlled trials, two interrupted time series, one descriptive study and six before and after intervention studies. Knowledge translation interventions were predominantly aimed at the treating clinician, with some targeting the organisation. Studies assessed effectiveness of interventions over 6-12 months in before and after studies, and 3-28 months in cluster or patient level controlled trials. Changes in clinical practice were variable, with studies on single disease and single treatments in a single site showing greater improvement. CONCLUSIONS: Evidence for effective methods to translate knowledge into practice in paediatric emergency medicine is fairly limited. More optimal study designs with more explicit descriptions of interventions are needed to facilitate other groups to effectively apply these procedures in their own setting.

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)0.016
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.177
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.276
GPT teacher head0.564
Teacher spread0.288 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations22
Published2016
Admission routes1
Has abstractyes

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