Applying Knowledge Translation Theory to Physical Therapy Research and Practice in Balance and Gait Assessment: Case Report
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Knowledge translation (KT) is an emerging discipline with a focus on implementing health evidence in decision making and clinical practice. Knowledge translation theories provide conceptual frameworks that can direct research focused on optimizing best practice. The objective of this case report is to describe one prominent KT theory--the knowledge-to-action (KTA) framework--and how it was applied to research on balance and gait assessment in physical therapist practice. CASE DESCRIPTION: Valid and reliable assessment tools are recommended to evaluate balance and gait function, but gaps in physical therapy practices are known. The KTA framework's 2-pronged approach (knowledge creation phase and action cycle) guided research questions exploring current practices in balance and gait assessment and factors influencing practice in Ontario, Canada, with the goal of developing and evaluating targeted KT interventions. OUTCOMES: Results showed the rate at which therapists use standardized balance and gait tools was less than optimal and identified both knowledge-to-practice gaps and individual and organizational barriers to implementing best assessment practices. These findings highlighted the need for synthesis of evidence to address those gaps prior to the development of potential intervention strategies. DISCUSSION: The comprehensive KTA framework was useful in guiding the direction of these ongoing research programs. In both cases, the sequence of the individual KTA steps was modified to improve the efficiency of intervention development, there was a need to go back and forth between the 2 phases of the KTA framework, and additional behavior change and barrier assessment theories were consulted. Continued research is needed to explicitly evaluate the efficacy of applying KT theory to best practice in health care.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".