Perceived Barriers to Physical Activity Among Youth Living in Rural and Urban Canadian Communities: A Nationally Representative Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: We used nationally representative data to explore associations among location of residence (rural/urban) and perceived barriers to physical activity (PA) in Canadian youth. METHODS: We analyzed the 2017 Canadian Community Health Survey, Barriers to Physical Activity Rapid Response data for 12- to 17-year-old youth. Nine items from the survey assessing perceived barriers to PA were combined into 3 barrier domains: resources, motivational, and socioenvironmental. The likelihood of reporting barriers to PA based on rural-urban location was examined using survey-weighted binary logistic regression following a model fitting approach. Sociodemographic factors were modeled as covariates and tested in interaction with location. For each barrier domain, we derived the best-fitting model with fewest terms. RESULTS: There were no location-specific effects related to reporting any barrier or motivation-related PA barriers. We found a sex by location interaction predicting the likelihood of reporting resource-related barriers. Rural boys were less likely to report resource-related barriers compared with urban boys (odds ratio [OR] = 0.42 [0.20, 0.88]). Rural girls were more likely to report resource-related barriers compared with boys (OR = 3.72 [1.66, 8.30]). Regarding socioenvironmental barriers, we observed a significant body mass index by location interaction demonstrating that rural youth with body mass index outside the "normal range" showed a higher likelihood of reporting socioenvironmental barriers compared with urban youth (OR = 2.38 [1.32, 4.30]). For urban youth, body mass index was unrelated to reporting socioenvironmental barriers (OR = 1.07 [0.67, 1.71]). CONCLUSION: PA barriers are not universal among Canadian youth. Our analyses highlight the importance of testing interactions in similar studies as well as considering key sociodemographic characteristics when designing interventions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".