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Knowledge translation: translating research into policy and practice

2015· article· en· W2259331011 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRevista gaúcha de enfermagem · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKnowledge translationKnowledge managementContext (archaeology)Knowledge sharingProcess (computing)Action (physics)Action researchComputer sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: This paper provides a theoretical-reflective study of knowledge translation concepts and their implementation processes for using research evidence in policy and practice. RESULTS: The process of translating research into practice is iterative and dynamic, with fluid boundaries between knowledge creation and action development. Knowledge translation focuses on co-creating knowledge with stakeholders and sharing that knowledge to ensure uptake of relevant research to facilitate informed decisions and changes in policy, practice, and health services delivery. In Brazil, many challenges exist in implementing knowledge translation: lack of awareness, lack of partnerships between researchers and knowledge-users, and low research budgets. CONCLUSIONS: An emphasis on knowledge translation has the potential to positively impact health outcomes. Future research in Brazil is needed to study approaches to improve the uptake of research results in the Brazilian context.

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)0.023
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.923
GPT teacher head0.779
Teacher spread0.144 · 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