Community size and sport on exercise participation across 28 countries
Bibliographic record
Abstract
Although sport participation is an important contributor to public health, there is little understanding of the social ecological factors that sustain sport participation. The objective of this study was to examine, across 28 countries, the association between community size and sport and exercise participation. Hierarchal non-linear Bernoulli modelling was used to examine the association between community size (1 >100,000; 0 < 100,000) and (1) individual sport, (2) team sport, and (3) exercise participation. After controlling for country-level clustering and a number of demographic variables, those residing in a community with less than 100,000 residents are more likely to participate in team sport (OR=1.14 95% CI= 1.02-1.27) and less likely to participate in exercise (OR=0.83 95% CI= 0.75-0.92) whereas community size is unrelated to individual sport participation (OR=0.98 95%CI= 0.88-1.11). Moreover, the associations between community size and individual sport, team sport, and exercise participation vary across countries (albeit marginally), suggesting these associations may be influenced by other socio-contextual factors. These findings provide novel evidence of a seemingly specific relationship between community size and team sport participation. Further cross-country research is needed to test this relationship and its underlying mechanisms. Acknowledgments: The first author is supported by a Joseph-Armand Bombardier Canada Graduate Doctoral Scholarship from the Social Sciences and Humanities Research Council (SSHRC 767-2012-1381) and by the Heart and Stroke Foundation of Canada and the CIHR Training Grant in Population Intervention for Chronic Disease Prevention: A Pan-Canadian Program (Grant #: 53893).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".