Perceived Walkability, Social Support, Age, Native Language, and Vehicle Access as Correlates of Physical Activity: A Cross-Sectional Study of Low-Socioeconomic Status, Ethnic, Minority Women
Bibliographic record
Abstract
BACKGROUND: The role of social-environmental factors in physical activity (PA) within lower income and ethnic minority populations is understudied. This study explored correlates of age-related PA and perceived walkability (PW). METHODS: Cross-sectional data (N = 401 women; ≥18 y) were collected within the Jane-Finch community in Toronto, Ontario using questionnaires. Generalized additive models, an extension to multiple regression, were used to estimate effect sizes and standard errors. RESULTS: Significant interactions between native language and car access (CA) were observed in PA variation across the lifespan. Individuals were evenly distributed across 4 comparison groups: 29.2% English-NoCA, 24.1% English-CA, 20.7% Non-English-NoCA, and 26.0% NonEnglish-CA. Risk of sedentariness increased with age for native English speakers > 50 years, but appears unaffected by age for other groups. English speakers without CA < 60 years appear least likely to be sedentary, followed by English speakers with CA. In general, an active individual at the 75th percentile of social support for exercise would have 1.62 (CI: 1.22-2.17) times the MET-Hours of PA than an active individual at the 25th percentile of SSE. CONCLUSIONS: English language facility and car access moderate relationships of social-environmental factors and PA. Further investigation is required to better understand correlates of PA for women in this demographic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".