Sport Fields as Potential Catalysts for Physical Activity in the Neighbourhood
Bibliographic record
Abstract
Physical activity is associated with access to recreational facilities such as sports fields. Because it is not clear whether objectively- or subjectively-assessed access to facilities exerts a stronger influence on physical activity, we investigated the association between the objective and perceived accessibility of sport fields and the levels of self-reported physical activity among adults in Edmonton, Canada. A sample of 2879 respondents was surveyed regarding their socio-demographics, health status, self-efficacy, levels of physical activity, as well as their perceptions of built environment in relation to physical activity. Neighbourhood-level data were obtained for each respondent based on their residence. Accessibility to facilities was assessed using the enhanced Two-Step Floating Catchment Area method. Geographic Information Systems were employed. A logistic regression was performed to predict physical activity using individual- and neighbourhood-level variables. Women, older individuals, and individuals with higher educational attainment were less likely to be physically active. Also, individuals with higher self-efficacy and higher objectively-assessed access to facilities were more likely to be physically active. Interventions that integrate provision of relevant programs for various population groups and of improved recreational facilities may contribute to sport fields becoming catalysts for physical activity by generating movement both on the site and in the neighbourhood.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".