Barriers and Facilitators to Using Knee Gait Analysis Report Findings in Physiotherapy Practice
Bibliographic record
Abstract
Background: Gait analysis can be used by physiotherapists to better understand the causes and consequences of knee pain. However, its use is not widespread among clinicians working with this clientele. Objective: To identify the barriers and facilitators to using a gait analysis data report in the evaluation and treatment of patients with knee pain. Design: A qualitative descriptive study design was used. Methods: Eleven physiotherapists received training on the principles of knee gait analysis assessment and data interpretation. Each physiotherapist was instructed to send two knee patients for a gait analysis assessment and then incorporate these new data into their practice with these patients. A semi-structured interview was conducted to ascertain the physiotherapist’s perception of the barriers and facilitators to using gait analysis. The verbatim transcripts were analyzed using content analysis software (NVivo 9). Results: The main barriers were as follows: 1) difficulty interpreting the gait analysis data report; 2) gait analysis testing procedures appear lengthy and complex; and 3) cost involved. The facilitators were: 1) gait analysis is perceived as being useful, especially for complex cases; 2) assessment protocol and data are perceived as valid and reliable; and 3) favorable perception of kinematic analysis by work colleagues. Conclusion: We were able to pinpoint the barriers and facilitators likely to promote the use of gait analysis in physiotherapy practice among knee injury patients. These barriers and facilitators are more related to the potential user (physiotherapist) and to the organizational and human environment than to gait analysis itself.
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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 | 0.038 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".