Associations between neighbourhood characteristics and community mobility in older adults with chronic health conditions
Bibliographic record
Abstract
PURPOSE: To explore associations between perceptions of neighbourhood built and social characteristics and satisfaction with community mobility in older adults with chronic health conditions. METHOD: Two hundred and thirty-seven community-dwelling adults aged 60 years or more with one or more of arthritis (osteoarthritis or rheumatoid arthritis), chronic obstructive pulmonary disease, diabetes or heart disease completed a cross-sectional, mailed survey. The survey addressed community mobility and 11 neighbourhood characteristics: amenities (three types), problems (six), social cohesion and safety. Analysis involved logistic regression modeling for each neighbourhood characteristic. RESULTS: Satisfaction with community mobility was associated with perception of no traffic problems (OR = 3.0, 95% CI = 1.4-6.2, p ≤ 0.05) and neighbourhood safety (OR = 3.4, 95% CI = 1.2-9.8, p ≤ 0.05), adjusted for age, ability to walk several blocks and depressive symptoms. CONCLUSION: Satisfaction with community mobility is associated with neighbourhood safety and no traffic problems among older adults with chronic conditions. While further research is needed to explore these neighbourhood characteristics in more detail and to examine causation, addressing these neighbourhood characteristics in health services or community initiatives may help promote community mobility in this population. Implications for Rehabilitation Community mobility, or the ability to move about one's community, is a key aspect of participation that enables other aspects of community participation. Good community mobility is associated with perception of no traffic problems and neighbourhood safety among older adults. Considering and addressing a broad range of environmental influences has the potential to improve community mobility in older adults, beyond traditional approaches. Health professionals can work with clients to develop strategies to avoid traffic and safety problems and can work with communities to develop safe spaces within neighbourhoods, to improve community mobility in older adults.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".