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Record W2615737213

Transferencia del conocimiento: el papel de las guías de práctica clínica

2016· article· es· W2615737213 on OpenAlex
Iván D. Flórez, Melissa Brouwers

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

VenueIATREIA · 2016
Typearticle
Languagees
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScope (computer science)Knowledge translationPsychological interventionHealth careProcess (computing)MedicineKnowledge managementSociologyPolitical scienceComputer scienceNursing
DOInot available

Abstract

fetched live from OpenAlex

Despite advances in health research in recent decades there are still gaps between knowledge and daily practice. Several terms have been used in literature to refer to the research that aims at reducing such gaps. Knowledge Translation (KT), the term used in this review, is defined as a dynamic and iterative process that includes synthesis, dissemination, exchange, and ethically-sound application of knowledge to improve health, provide more effective health services and products and strengthen the health care system. This article reviews basic aspects of KT, its differences with translational research, the conceptual framework on which the KT is supported, its scope and objectives, the tools used for the translation, the possible barriers and interventions to counteract them. Clinical practice guidelines, which are based on systematic reviews of the literature, are the ideal tools to synthesize, analyze and contextualize the evidence with the aim to create recommendations, which are the first step when we are interested in planning a KT intervention to be implemented in a target audience.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.119
GPT teacher head0.496
Teacher spread0.377 · 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