Knowledge translation in physical therapy: from theory to practice
Bibliographic record
Abstract
PURPOSE: Knowledge translation (KT) has emerged as a concept that can lead to a greater utilization of evidence-based research in systems of care. Despite a rise in KT research, the literature on KT in relation to physical therapy practice is scarce. This article provides physical therapists (PTs) with recommendations that can support the effective implementation of new knowledge and scientific evidence in clinical practice. METHOD: Recommendations are grounded in the Ottawa Model of Research Use and in the literature in KT in the health professions. RESULTS: A well-established KT process, which is supported by a planning model, is essential to guide the implementation of scientific evidence. Consensus among all stakeholders about what evidence will be implemented must be reached. Context-related barriers and facilitators should be assessed and tailored active and multi-component interventions should be considered. Participation from individuals in intermediary positions (e.g. opinion leaders) supports implementation of KT interventions. Monitoring of the process and assessment of intended outcomes should be performed in order to assess the success of the implementation. CONCLUSION: Five major recommendations grounded in the Ottawa model are provided that can assist PTs with the complex task of implementing new knowledge in their clinical practice. IMPLICATIONS FOR REHABILITATION: In order to support EBP, knowledge translation interventions can be used to support best practice. Implementation of new knowledge should be guided by a framework or a conceptual model. Consensus on the evidence must be reached and assessment of context-related factors should be done prior to the implementation of any KT intervention. Intervention strategies should be active, multi-component and include individuals with intermediary positions that can facilitate the KT process.
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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 | 0.163 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".