Life‐Space Mobility and Parkinson's Disease. A Multiple‐Methods Study
Bibliographic record
Abstract
Background: Life-space mobility (LSM) captures a broad spectrum of mobility in physical and social environments; however, it has not been extensively studied in Parkinson's disease. Using a multiple-methods approach, individual, social and environmental factors that impact LSM were explored in PD. Methods: Two hundred twenty-seven participants with PD (n = 113) and a comparative group without PD (n = 114) were recruited from the community. Within a cross-sectional survey, LSM (University of Alabama Birmingham Life-Space Assessment, LSA) was compared in the two groups. Using multiple linear regression, socio-demographics, lifestyle behaviors, medical, mobility and social factors were examined to identify factors that explained LSM. A qualitative narrative inquiry was completed to augment the findings from the survey; 10 participants with PD were interviewed regarding facilitators and barriers to mobility. Results: The mean overall LSA-composite score for the PD group was 64.2 (SD = 25.8) and 70.3 (SD = 23.1) for the community comparative group (mean difference = 6 points, 95%CI:-0.4, 12.5) indicating most participants moved independently beyond their neighborhoods. A higher proportion of the PD group required assistance with mobility than the community comparison group. Not driving, receiving caregiving, lower social participation, and lower monthly family finances were associated with restricted LSM in the PD group. Data from qualitative interviews supported quantitative findings and offered insights into the features of the built environment that facilitate and restrict mobility. Conclusion: Individual, social and environmental factors are associated with the LSM among persons with PD. Clinicians and policy-makers should include both individual and community-based factors when developing interventions to encourage the LSM of the PD population.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".