The Contribution of Conceptual Frameworks to Knowledge Translation Interventions in Physical Therapy
Bibliographic record
Abstract
There is growing recognition of the importance of knowledge translation activities in physical therapy to ensure that research findings are integrated into clinical practice, and increasing numbers of knowledge translation interventions are being conducted. Although various frameworks have been developed to guide and facilitate the process of translating knowledge into practice, these tools have been infrequently used in physical therapy knowledge translation studies to date. Knowledge translation in physical therapy implicates multiple stakeholders and environments and involves numerous steps. In light of this complexity, the use of explicit conceptual frameworks by clinicians and researchers conducting knowledge translation interventions is associated with a range of potential benefits. This perspective article argues that such frameworks are important resources to promote the uptake of new evidence in physical therapist practice settings. Four key benefits associated with the use of conceptual frameworks in designing and implementing knowledge translation interventions are identified, and limits related to their use are considered. A sample of 5 conceptual frameworks is evaluated, and how they address common barriers to knowledge translation in physical therapy is assessed. The goal of this analysis is to provide guidance to physical therapists seeking to identify a framework to support the design and implementation of a knowledge translation intervention. Finally, the use of a conceptual framework is illustrated through a case example. Increased use of conceptual frameworks can have a positive impact on the field of knowledge translation in physical therapy and support the development and implementation of robust and effective knowledge translation interventions that help span the research-practice gap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".