MétaCan
Menu
Back to cohort

Knowledge Translation Consensus Conference: Research Methods

2007· article· en· W4238432572 on OpenAlexaff
Scott Compton, Eddy Lang, Thomas Richardson, Erik P. Hess, Jeffrey Green, William J. Meurer, Rachel Stanley, Robert Dunne, Shannon D. Scott, Rahul K. Khare, Jeremy Grimshaw

Bibliographic record

VenueAcademic Emergency Medicine · 2007
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of AlbertaUniversity of OttawaMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineSession (web analytics)BreakoutConsensus conferenceKnowledge translationPsychological interventionMedical educationMEDLINEEngineering ethicsKnowledge managementNursingWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

The authors facilitated a workshop session during the 2007 Academic Emergency Medicine Consensus Conference to address the specific research methodologies most suitable for studies investigating the effectiveness of knowledge translation interventions. Breakout session discussions, recommendations, and examples in emergency medicine findings are presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.432
metaresearch head score (Gemma)0.702
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score0.700

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4320.702
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0200.014
Science and technology studies0.0110.007
Scholarly communication0.0120.011
Open science0.0100.017
Research integrity0.0160.011
Insufficient payload (model declined to judge)0.0710.015

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 head0.819
GPT teacher head0.744
Teacher spread0.075 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations6
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueAcademic Emergency MedicineSame topicHealth Sciences Research and EducationFrench-language works237,207