“These Few Blocks, These Are My Village”: The Physical Activity and Mobility of Foreign-Born Older Adults
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The self-reported health of foreign-born older adults (FBOAs) is lower than that of nonimmigrant peers. Physical activity (PA) and mobility enhance health in older age, yet we know very little about the PA and mobility of FBOAs. In this analysis we sought to determine: (a) What factors facilitate PA amongst FBOAs? and (b) How do gender, culture, and personal biography affect participants' PA and mobility? RESEARCH DESIGN AND METHODS: We worked closely with community partners to conduct a mixed-method study in Vancouver, Canada. Eighteen visible minority FBOAs completed an in-depth interview in English, Cantonese, Mandarin, Punjabi, or Hindi. RESULTS: Three dominant factors promote participants' PA and mobility: (a) participants walk for well-being and socialization; (b) participants have access to a supportive social environment, which includes culturally familiar and linguistically accessible shops and services; and (c) gender and personal biography, including work history and a desire for independence, affect their PA and mobility behaviors. DISCUSSION AND IMPLICATIONS: We extend the Webber et al. mobility framework, with examples that further articulate the role of gender (e.g., domestic work), culture (cultural familiarity) and personal biography (work history and a desire for familial independence) (Webber, S. C., Porter, M. M., & Menec, V. H. (2010). Mobility in older adults: A comprehensive framework. The Gerontologist, 50, 443-450. doi:10.1093/geront/gnq013). Future programming to support the PA of FBOAs should be culturally familiar and linguistically accessible.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".