Barriers to physical activity for adults in rural and urban Canada: A cross-sectional comparison
Bibliographic record
Abstract
BACKGROUND: Individual differences in physical activity behavior are associated with a collection of individual and environmental factors manifesting as barriers to participation. Understanding how barriers to physical activity differ based on sociodemographic characteristics can support identification and elimination of health inequities. OBJECTIVES: To compare the odds of reporting individual and environmental barriers to physical activity in rural and urban adults, and explore interactions between rural-urban location and sociodemographic factors to characterize patterns in barriers to physical activity. DESIGN: Cross-sectional. METHODS: We analyzed the 2017 Canadian Community Health Survey Barriers to Physical Activity Rapid Response, with a final weighted sample of 24,499,462 (unweighted n=21,967). The likelihood of reporting each barrier domain based on rural-urban location was examined using binary logistic regression following a model-fitting approach with sociodemographic characteristics as covariates or interaction terms. RESULTS: Adjusting for sociodemographic factors, rural residents showed 85% higher odds of reporting at least one social or built environmental barrier (OR=1.85 [1.66, 2.07]). Compared to urban residents, rural residents showed significantly higher odds of reporting barriers to facility access (OR=4.15 [3.58, 4.83]) and a lack of social support to be active (OR=1.17 [1.04, 1.32]). Urban residents reported lower preference for physical activity, lower enjoyment of physical activity and lower confidence in their ability to regularly engage in physical activity. Interactions between socioeconomic status and location were identified related to enjoyment and confidence to be active. There was no effect of location on predicting the odds of reporting an individual resource-related variable (e.g., time, energy). CONCLUSIONS: Despite being more likely than urban residents to prefer and enjoy physical activity, rural residents have fewer opportunities and receive less social support to be active. It is important to consider geographic location when characterizing barriers to physical activity and in the development of context-specific health promotion strategies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".