Partnership in Action: An Innovative Knowledge Translation Approach to Improve Outcomes for Persons with Fibromyalgia
Bibliographic record
Abstract
The creation of new knowledge through health research often fails to translate into appropriate changes in health care practice.1 In fact, it may take 10 to 20 years for information gained through research to be implemented in routine clinical practice.1 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. 2 The Canadian Institutes of Health Research (CIHR) seek to close the KTA gap by accelerating the translation of new research knowledge into improved health care practices and outcomes-a process known as knowledge translation (KT).3 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).4 The 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.093 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".