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Record W2038428406 · doi:10.1002/chp.46

Continuing education, guideline implementation, and the emerging transdisciplinary field of knowledge translation

2006· article· en· W2038428406 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

VenueJournal of Continuing Education in the Health Professions · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of TorontoSt. Michael's HospitalOntario Medical AssociationMinistry of Health and Long Term Care
Fundersnot available
KeywordsTransformative learningGuidelineKnowledge translationContext (archaeology)Health carePerspective (graphical)Quality (philosophy)Best practiceMedicineMedical educationNursingKnowledge managementPsychologyEngineering ethicsPedagogyComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

This article discusses continuing education and the implementation of clinical practice guidelines or best evidence, quality improvement, and patient safety. Continuing education focuses on the perspective of the adult learner and is guided by well-established educational principles. In contrast, guideline implementation and related concepts borrow from the fields of quality improvement and patient safety and from health services research. Relative to the question of improved clinical outcomes, both to some extent afford only partial understanding of a complex issue. Knowledge translation (KT) is a transformative concept that links the best elements of both broad fields and, in particular, adds educational elements to the work of health services researchers and others. Interdisciplinary in the extreme, KT is explored in some detail: its major elements (information, facilitation, context, the clinician-learner, among others) considered as variables in an equation leading to knowledge uptake and improved health care outcomes and an improved functioning health care system.

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)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
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 science0.0000.000
Research integrity0.0000.001
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 head0.075
GPT teacher head0.547
Teacher spread0.473 · 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