Physiotherapists’ understanding and assessment of gait stability: a qualitative study
Bibliographic record
Abstract
PURPOSE: Maintaining a stable gait is required to safely navigate our environments, however, little is known about what physiotherapists consider when determining an older adult's gait stability. Thus, the objective of this study was to describe how physiotherapists understand and assess gait stability. MATERIALS AND METHODS: Twenty-six Canadian registered physiotherapists with clinical experience in gerontology participated in a semi-structured interview. Physiotherapists were asked about their understanding of gait stability and shared their approach to assessment using videos of older adults walking. Physiotherapists then used a standardized definition of gait stability and re-assessed gait stability. Interviews were analyzed using conventional and summative content analysis. RESULTS: There was no common understanding of gait stability amongst participants, and their assessments included these features: managing disturbances to balance, movement of the trunk and arms, gait mechanics, level of independence, confidence, cognition and insight and medical conditions. Upon receiving a standardized definition of gait stability, participants more consistently discussed how gait impairments impacted resilience to perturbations. CONCLUSIONS: It is important for physiotherapists to gain familiarity with how to assess gait stability. This study highlights the need for translating gait stability knowledge into practice.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".