Using Mixed Methods to Explore Older Residents' Physical Activity and Experiences of Community Active Aging Friendliness
Bibliographic record
Abstract
Physical inactivity is the fourth leading risk factor for global mortality and as such, it is critical that physical activity guidelines for health consider equitable access to physical activity opportunities for all, including those aged 65 years and older. The purpose of this study was to examine the intersections of attributes of older people (e.g. income, gender, age, health, physical activity) and attributes of the places they live (e.g. rurality, public transit, sidewalks, parks, community centre) to explain older adults’ likelihood of participating in physical activities recommended for health. The study employed a sequential mixed-methods design. We examined the interplay of self-reported walking and exercise behaviours in a representative sample of independent, older adult (n=126; age 65 years and older) residents with physical activity features of their rural (n=3) and urban (n=3) communities. Computed logistic regressions models predicted survey respondents' reports of walking and exercise by community type. Quantitative findings were integrated with voiced experiences of community features as qualitative determinants of active aging provided by adult residents (n=237; ages 50 and older). Qualitative data were coded and analysed using constant comparison triangulation across data sources. Qualitative data consisted of mapped photographs of observable determinants, physical activity supports and barriers, and transcribed focus group narratives generated by local residents. Older adults living in more urban (vs. rural) communities reporting good health and higher income had greater odds of walking around their neighbourhood (p<0.001). Women (vs. men) reporting good health and higher income had greater odds (p<0.05) of exercising regardless of community type. Rural (vs. urban) communities were described as having fewer available, accessible, and affordable supports, and more barriers for walking and exercising. Our data suggest active aging initiatives should include socio-environmental strategies and address resource inequities to enable participation across geographic, economic, gender, and health status differences among older adults. In particular, living in a rural place, being low-income and of poor health were associated with lower odds of walking or doing other forms of exercise, fewer supports and more socio-environmental barriers to active aging for older adults in our study.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".