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Record W2104505650 · doi:10.3747/co.19.985

Challenges in Knowledge Translation: The Early Years of Cancer Care Ontario’s Program in Evidence-Based Care

2012· article· en· W2104505650 on OpenAlexafffundvenueabout
George P. Browman

Bibliographic record

VenueCurrent Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersCanadian Institutes of Health ResearchPartenariat Canadien Contre Le CancerOntario Ministry of Health and Long-Term CareCanadian Centre for Applied Research in Cancer ControlCancer Care Ontario
KeywordsKnowledge translationContext (archaeology)MedicineSkepticismGuidelineMedical educationPublicationPublic relationsKnowledge managementPolitical scienceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer Care Ontario's Program in Evidence-Based Care (pebc) was formalized in 1997 to produce clinical practice guidelines for cancer management for the Province of Ontario. At the time, the gap between guideline development and implementation was beginning to be acknowledged. The Program implemented strategies to promote use of guidelines. METHODS: The program had to overcome numerous social challenges to survive. Prospective strategies useful to practitioners-including participation, transparent communication, a methodological vision, and methodology skills development offerings-were used to create a culture of research-informed oncology practice within a broad community of practitioners.Reactive strategies ensured the survival of the program in the early years, when some within the influential academic community and among decision-makers were skeptical about the feasibility of a rigorous methodologic approach meeting the fast turnaround times necessary for policy. RESULTS: The paper details the pebc strategies within the context of what was known about knowledge translation (kt) at the time, and it tries to identify key success factors. CONCLUSIONS: Many of the barriers faced in the implementation of kt-and the strategies for overcoming them-are unavailable in the public domain because the relevant reporting does not fit the traditional paradigm for publication. Telling the "stories behind the story" should be encouraged to enhance the practice of kt beyond the science.

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 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.793
GPT teacher head0.628
Teacher spread0.165 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2012
Admission routes4
Has abstractyes

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