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Record W2026713631 · doi:10.3138/physio.61.3.123

Partnership in Action: An Innovative Knowledge Translation Approach to Improve Outcomes for Persons with Fibromyalgia

2009· article· en· W2026713631 on OpenAlexaffvenue
Mary Brachaniec, Vincent DePaul, Margaret Elliott, Lynn Moore, Pamela Sherwin

Bibliographic record

VenuePhysiotherapy Canada · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster UniversityCochraneCanadian Physiotherapy AssociationCrandall UniversityArthritis SocietyUniversity of TorontoCanadian Institutes of Health ResearchCanadian Arthritis Patient Alliance
Fundersnot available
KeywordsKnowledge translationDisseminationBest practiceHealth careFibromyalgiaGeneral partnershipMedicineMedical educationInformation DisseminationAction (physics)PsychologyNursingKnowledge managementComputer sciencePhysical therapyPolitical science

Abstract

fetched live from OpenAlex

The creation of new knowledge through health research often fails to translate into appropriate changes in health care practice. In fact, it may take 10 to 20 years for information gained through research to be implemented in routine clinical practice. The difference between the best (evidence-based) care and traditional care was referred to by Graham and colleagues as the ''knowledge to action (KTA) '' gap. 2 This gap accounts for a significant number of patients who do not receive the best possible care or, in some cases, receive care that is potentially harmful. Below we introduce the concept of KT, as defined and used by CIHR, and outline an innovative project to disseminate the results of a 2007 Cochrane Systematic Review on the effects of exercise in fibromyalgia (FM). he collaborative work conducted by a small CIHR subgroup to disseminate research priorities, as identified by review authors, in a user-friendly format to the FM research community should be of particular interest to physiotherapists.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.645
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.474
GPT teacher head0.614
Teacher spread0.140 · 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

Citations11
Published2009
Admission routes2
Has abstractyes

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