How Societal Forces of Change Are Transforming Youth Physical Activity Promotion in North America
Bibliographic record
Abstract
BACKGROUND: Climate change, increasing recognition of institutionalized discrimination, and the COVID-19 pandemic are large-scale, societal events (ie, forces of change) that affect the timing, settings, and modes of youth physical activity. Despite the impact that forces of change have on youth physical activity and physical activity environments, few studies consider how they affect physical activity promotion. METHODS: The authors use 2 established frameworks, the ecological model of physical activity and the youth physical activity timing, how, and setting framework, to highlight changes in physical activity patterns of youth in North America that have resulted from contemporary forces of change. RESULTS: North American countries-Canada, Mexico, and the United States-have faced similar but contextually different challenges for promoting physical activity in response to climate change, increasing recognition of institutionalized discrimination, and the COVID-19 pandemic. Innovative applications of implementation science, digital health technologies, and community-based participatory research methodologies may be practical for increasing and sustaining youth physical activity in response to these forces of change. CONCLUSIONS: Thoughtful synthesis of existing physical activity frameworks can help to guide the design and evaluation of new and existing physical activity initiatives. Researchers, practitioners, and policymakers are encouraged to carefully consider the intended and unintended consequences of actions designed to respond to forces of change.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".