The longitudinal evaluation of the Canadian 24-Hour Movement Guidelines for Adults: lessons learned and considerations for future research
Bibliographic record
Abstract
National movement behaviour guidelines, in isolation, are unlikely to influence practice or policy unless accompanied by robust knowledge mobilization (KMb) strategies. However, without pairing KMb strategies with systematic evaluation, the impact of large-scale dissemination is unknown. The objectives of this study were to (1) systematically assess the dissemination of the Canadian 24-Hour Movement Guidelines for Adults Aged 18-64 Years and Adults Aged 65 Years or Older (24HMG) using the Reach Effectiveness Adoption Implementation and Maintenance (RE-AIM) framework and (2) provide recommendations to support the KMb and future impact of national movement behaviour guidelines. Intermediary organizations involved in the development and dissemination of the 24HMG were invited to participate in this study. A combination of methods-including cross-sectional surveys, media monitoring, and website content analysis-were performed at multiple timepoints over a 12-month period to assess the dissemination of the 24HMG. Findings suggest that the multi-pronged dissemination approach used for the 24HMG had a large reach to guideline target audiences (approximately 11.9 million) but resulted in low awareness and knowledge of the 24HMG among adults living in Canada (31.9% and 1.6%, respectively). Dissemination activities performed by intermediary organizations peaked in the first 4-months post-guideline release (76% of responding organizations), trending downwards over time (53% of responding organizations at 12 months). The complexity of disseminating national movement behaviour guidelines presents many challenges to systemic adoption. However, the impact of future national movement behaviour guidelines may be improved by augmenting current dissemination approaches to include coordinated, scalable, and capacity-building strategies.
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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 | 0.313 | 0.453 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".