LIFE-SPACE MOBILITY IN THE CANADIAN LONGITUDINAL STUDY ON AGING: A MULTI-DISCIPLINARY PERSPECTIVE
Bibliographic record
Abstract
Factors that may hinder or support the independent life-space mobility of older adults are complex and multi-factorial. However, existing studies have focused on a narrow group of factors that are often discipline specific (i.e. biomechanics, biological, etc.). A multi-disciplinary and comprehensive assessment of life-space mobility is needed to optimize opportunities for healthy aging and prevent mobility decline in older adults. As such, the purpose of this study was to examine how biological, psychosocial, physical, cognitive, environmental, and financial factors influence life space mobility in a large population-based sample of community dwelling older adults. For this, we employed the Canadian Longitudinal Study on Aging (CLSA) baseline comprehensive dataset version 3.2, that includes performance-based testing and in-depth questionnaires on more than 30,000 adults aged 45–85. Multivariate regression was carried out to identify variables that were significantly associated with the Life-Space Index (LSI). Analyses were adjusted for age, sex, culture and the presence of chronic conditions. The sample was 51% women, with a mean age of 63 years and mean LSI score of 90 (range 24 to 120). Physical (4-Meter Timed Walk Test Beta (ß)=-2.2 and lung function ß=1.3), psychosocial (depression ß=-0.5, social support ß=2.3), cognitive (ß=0.6), environmental (urban vs. rural ß=8.8) and financial (income ß=9.6) factors were significantly associated with the LSI when adjusted for covariates. This study demonstrated that determinants of life-space mobility are multi-factorial and span several disciplines. Validation of these results with longitudinal data may provide insight into management strategies for preserving life-space mobility.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".