Re-assessing the social climate of physical (in)activity in Canada
Bibliographic record
Abstract
Social-ecological models suggest that a strategy for increasing population physical activity participation is to reconstruct the "social climate" through changing social norms and beliefs about physical activity (PA). In this study, we assessed whether the PA social climate in Canada has changed over a five-year period after controlling for sociodemographic factors and PA levels. Replicating a survey administered in 2018, a sample of adults in Canada (n = 2,507) completed an online survey assessing social climate dimensions, including but not limited to descriptive and injunctive norms. Descriptive statistics were calculated, and binary logistic regressions were conducted to assess the associations of sociodemographic factors and year of the survey with social climate dimensions. Results suggest some social climate constructs are trending in a positive direction between 2018 and 2023. Physical inactivity was considered a serious public health concern by 49% of respondents, second to unhealthy diets (52%). Compared to those who participated in the 2018 survey, participants in 2023 were less likely to see others walking or wheeling in their neighbourhood (OR = 1.58, 95% CI: 1.41, 1.78), but more likely to see people exercising (OR = 0.82, 95% CI: 0.73, 0.92) and kids playing in their neighbourhood (OR = 0.75, 95% CI: 0.66, 0.85). No changes were reported between 2018 and 2023 in individuals' perceptions of whether physical inactivity is due to individual versus external factors (OR = 0.99, 95% CI: 0.87, 1.13). The findings of this work indicate a modest positive shift in some measured components of the social climate surrounding PA although attributing causes for these changes remain speculative.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".