Understanding the contribution of public- and restricted-access places to overall and domain-specific physical activity among Mexican adults: A cross-sectional study
Bibliographic record
Abstract
Latin Americans engage in physical activity (PA) in unique ways and use a wider range of places for PA than those commonly studied in high-income settings. We examined the contribution of a variety of places and domains of PA to meeting PA recommendations among a sample of adults (18-65 y) from all over Mexico. This was a cross-sectional study conducted in 2017 (n = 3 686). Overall and domain-specific PA was measured using the Global Physical Activity Questionnaire. Use of places for PA was self-reported. Places were classified as private or public. In 2018, associations between specific places and meeting PA recommendations (≥150 mins/week) were estimated using multivariate logistic regression models. In total 72.1% met PA recommendations. The proportion meeting recommendations through domain-specific PA was highest for leisure-time PA (50.0%), followed by travel-related (39.1%) and work-related (24.9%) PA. The most commonly reported places for PA were home (43%), parks (40.7%) and streets (39.4%) (public). Use of most public places was positively associated with meeting PA recommendations, mainly through travel-related PA (Streets OR 2.05 [95% CI 1.71-2.45]; Cycling paths OR 1.91 [1.37-2.68]). Using private places was more strongly associated with PA, mainly leisure-time PA (Gyms OR 9.66 [7.34-12.70]); Sports facilities OR 5.03 [3.27-7.74]). In conclusion, public and private places were important contributors to PA. While public places may be a powerful setting for PA promotion, increasing the equitable access for all to private places may also represent an effective strategy to increase PA among Mexican adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".