Results From Japan’s 2016 Report Card on Physical Activity for Children and Youth
Bibliographic record
Abstract
BACKGROUND: The Report Card on Physical Activity for Children and Youth aims to consolidate existing evidence, encourage greater evidence-informed physical activity, and improve surveillance of physical activity. METHODS: The Japan report card followed the methodology of the Canadian and Scottish report cards, but was adapted to reflect the Japanese context. Nationally representative data were used to score each of the respective indicators. RESULTS: The 2016 Japan Report Card on Physical Activity for Children and Youth consists of Health Behaviors and Outcomes (7 indicators), and Influences on Health Behaviors (4 indicators). Three Health Behaviors and Outcomes received C grades (Participation in Sport; Sedentary Behavior; Recreational Screen Time; Physical Fitness), while 2 indicators could not be graded (Overall Physical Activity, and Active Play). The indicators Active Transportation (B) and Weight Status were favorable (A). In the Influences domain, Family Influence and Community and the Built Environment were graded as D, while School and Government Strategies and Investments were favorable (B). CONCLUSIONS: The Japan report card illustrated some favorable health behaviors, health outcomes, and influences. There is a need for more evidence especially on overall physical activity levels, active play, and community and the built environment.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".