Impact of the Active Healthy Kids Canada Report Card: A 10-Year Analysis
Bibliographic record
Abstract
For 20 years Active Healthy Kids Canada (AHKC) has worked to inspire the country to engage all children and youth in physical activity (PA). The primary vehicle to achieve this is the AHKC Report Card on Physical Activity for Children and Youth, which has been released annually since 2005. Using 10 years of experience with this knowledge translation and synthesis mechanism, this paper aggregates and consolidates diverse evidence demonstrating the impact of the Report Card and related knowledge translation activities. Over the years many evaluations, consultations, assessments, and surveys have helped inform changes in the Report Card to improve its impact. Guided by a logic model, the various assessments have traversed areas related to distribution and reach, meeting stakeholder needs, use of the Report Card, its influence on policy, and advancing the mission of AHKC. In the past 10 years, the Report Card has achieved > 1 billion media impressions, distributed > 120,000 printed copies and > 200,000 electronic copies, and benefited from a collective ad value > $10 million. The Report Card has been replicated in 14 countries, 2 provinces, 1 state and 1 city. AHKC has received consistent positive feedback from stakeholders and end-users, who reported that the Report Card has been used for public awareness/education campaigns and advocacy strategies, to strengthen partnerships, to inform research and program design, and to advance and adjust policies and strategies. Collectively, the evidence suggests that the Report Card has been successful at powering the movement to get kids moving, and in achieving demonstrable success on immediate and intermediate outcomes, although the long-term goal of improving the PA of Canadian children and youth remains to be realized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".