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Evaluation of a knowledge translation and exchange platform to advance non-communicable disease prevention

2015· article· en· W2528025917 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEvidence & Policy · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchDeakin UniversityAustralian Government
KeywordsKnowledge translationKnowledge managementProcess (computing)Psychological interventionComputer scienceKnowledge transferMedical educationMedicinePsychologyApplied psychologyNursing

Abstract

fetched live from OpenAlex

Coordinated systems are required to ensure evidence-informed practice and evaluation of community-based interventions (CBIs). Knowledge translation and exchange (KTE) strategies show promise, but these require evaluation. This paper describes implementation and evaluation of COOPS, a national KTE platform to support best practice in obesity prevention CBIs. A logic model guides KTE activities including knowledge brokering, networking, tailored communications, training, and needs assessments. A mixed-methods evaluation includes communications data, knowledge brokering database, annual survey of CBIs, pre- and post-event questionnaires, interviews, social network analysis, and case studies. This evaluation will contribute to understanding the process of implementing a KTE platform with CBIs and its reach, quality and effectiveness.

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.

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)0.011
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.868
GPT teacher head0.723
Teacher spread0.145 · 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