A scoping review of safe mobility behaviour in fall prevention: implications for people with Parkinson’s disease
Bibliographic record
Abstract
PURPOSE: Falls are a major concern for people with Parkinson's disease (PwPD) due to associated motor and non-motor impairments. Promoting safe mobility behaviour may be an effective fall prevention intervention, however this concept is poorly articulated in the literature. The aim of this scoping review was to map out the definition and concepts of safe mobility behaviour to draw implications for PwPD. MATERIALS AND METHODS: The Joanna Briggs Institute methodology for scoping reviews was followed. Studies involving older adults (aged ≥ 65 years) and/or PwPD that sought to define, describe, and/or explain this concept were included. RESULTS: Of the 21,936 records retrieved, 124 publications were included. No studies defined safe mobility behaviour. However, its performance was described as a combination of observable actions and cognitive processes. Mobility behaviour was influenced by an interaction between the person, environment, and task performance. CONCLUSION: We propose a definition for safer mobility behaviour as any protective action and associated functional cognitive process used to reduce the likelihood of a fall during mobility-related activities. It is unique to each person and occurs across a continuum of safer to riskier behaviour. Future research developing and testing interventions targeting safer mobility behaviour for PwPD is warranted.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".