From laboratory to community: Three examples of moving evidence‐based physical activity into practice in Canada
Bibliographic record
Abstract
Physical activity (PA) is important for enhancing and sustaining people's health and well-being. Although a number of efficacious PA interventions have been developed, few have been translated from research into practice. Consequently, the knowledge-to-practice gap continues to grow, leaving many individuals unable to access evidence-based PA opportunities. This gap may be particularly relevant for those who grapple with poor health due to intrapersonal, interpersonal, cultural and system-level barriers that limit their access to evidence-based PA opportunities. Implementation efforts designed to bring research into real-world settings may bridge the knowledge-to-practice gap. Yet, cultivating quality partnerships and ensuring effectiveness, methodological rigour and scalability in real-world settings can be difficult. Furthermore, researchers seldom publish examples of how they addressed these challenges and translated their evidence-based PA opportunities into practice. Herein, we describe three cases of successful PA implementation among diverse populations: (a) individuals affected by cancer, (b) adults living with prediabetes, and (c) children from under-resourced communities. Commonalities across cases include guiding theories and frameworks, the strategies to facilitate and maintain partnerships, and scalability and sustainability plans. Practical tips and recommendations are provided to spur research and translation efforts that consider implementation from the outset, ultimately ensuring that people receive the benefits PA can confer.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".