Life‐space mobility, balance, and self‐efficacy in Parkinson disease: A cross‐sectional study
Bibliographic record
Abstract
Abstract Background Life‐space mobility (LSM) is a mobility measure that assesses the physical and social environments through which people move during their daily lives. Objective To characterize LSM among individuals with Parkinson disease and explore the relationship between LSM, self‐efficacy, and balance. Design A cross‐sectional study. Settings Movement disorder clinic at a teaching hospital. Participants Eighty‐eight participants with Parkinson disease. Interventions Not applicable. Main Outcome Measures The dependent variable (LSM) was assessed using the Life‐Space Assessment (LSA) instrument. Balance evaluation and balance self‐efficacy were assessed using the Mini Balance Evaluation Systems Test (Mini‐BESTest) and the Activities‐Specific Balance Confidence Scale, respectively. Other variables, such as age, disease staging (Hoehn‐Yahr staging system), cognition (Montreal Cognitive Assessment), and depressive symptoms (Beck Depression Inventory‐II), were also measured. Results The mean LSA score was 65.2 (SD: 22.8) and mean age was 63.2 years (SD: 10.5 years). Among the 88 patients, 32 (36.4%) were classified as restricted LSM. Age (p = .03), disease severity (p = .02), cognition (p = .02), and motor subtype (p = .006) were associated with more restricted LSM among participants. A multiple linear regression model demonstrated that LSM can be predicted by balance performance (R2 = 0.377; p < .001). Conclusion Age, disease severity, cognition, motor subtype, balance self‐efficacy, and balance performance are associated with LSM. Understanding and improving balance and self‐efficacy in people with Parkinson disease could facilitate community mobility and promote functional independence and health maintenance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".