Results From the First French Report Card on Physical Activity for Children and Adolescents
Bibliographic record
Abstract
BACKGROUND: Many countries publish periodic Report Cards on physical activity for children and youth. This paper presents the results from the first French Report Card providing a systematic synthesis and assessment of the national engagements to facilitate childhood physical activity. METHODS: A search for nationally representative data on 8 indicators of physical activity was conducted and the data were assessed by an expert panel according to international procedures. Whether children across France are achieving specific benchmarks was rated using an established grading framework [A, B, C, D, F, or INC (incomplete)]. Data were interpreted, grades assigned and detailed in the 2016 Report Card that was produced and disseminated. RESULTS: The expert panel awarded the following grades: Overall Physical Activity: INC; Organized Sport Participation: D; Active Transportation: D; Sedentary Behaviors: D; Family and Peers: INC; School: B; Community and the Built Environment: INC; Government Strategies and Investment: INC. CONCLUSIONS: The grades reveal that efforts must be done to improve youth's physical activity and that several gaps in the literature still need to be addressed. Collectively the results highlight that children's physical activity levels are low and that further national supports and investments are needed to promote childhood healthy active living in France.
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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 | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".