COMPREHENSIVE MOBILITY FRAMEWORK VERIFICATION USING CANADIAN LONGITUDINAL STUDY ON AGING DATA
Bibliographic record
Abstract
Abstract Mobility within and between life spaces (one’s room, home, local community, and further destinations) positively influences quality of life and health in older adults. Understanding factors that impact mobility is relevant in many contexts (e.g., in clinical and research environments, for city-planning and social programming). The purpose of this study was to verify a comprehensive theoretical framework for mobility (Webber et al., 2010) with structural equation modelling using data from the Canadian Longitudinal Study on Aging (CLSA). We estimated associations between latent factors consisting of physical, psychosocial, environmental, financial, and cognitive attributes, and life space mobility for participants 65-85 years of age (n=11,667, mean age 73 ± 6 years) with age, sex and education as covariates. The model demonstrated good fit (CFI = 0.90, RMSEA (90% CI) = 0.025 (0.024, 0.026)). Physical, psychosocial, and cognitive health were positively associated with life space mobility. Being less afraid to walk after dark (environmental variable) was also associated with greater mobility. Financial status influenced mobility through positive associations with psychosocial health and physical health. Higher education was related to better cognitive function and better financial status. Age was indirectly associated with life space mobility through its negative associations with financial status, cognition, and physical health. The same model can be used for males and females. The comprehensive mobility framework was verified with the large population-based CLSA dataset. Continued use of the framework is supported as it accurately portrays the complexity of factors that influence older adults’ 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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".