Results From India’s 2016 Report Card on Physical Activity for Children and Youth
Bibliographic record
Abstract
BACKGROUND: Physical inactivity in children and youth in India is a major public health problem. The 2016 Indian Report Card on Physical Activity for Children and Youth has been conceptualized to highlight this epidemic by appraising behaviors, contexts, strategies, and investments related to physical activity of Indian children and youth. METHODS: An international research collaboration resulted in the formation of a Research Working Group (RWG). RWG determined key indicators; identified, synthesized, and analyzed existing evidence; developed criteria for assigning grades; and, finally, assigned grades to indicators based on consensus. RESULTS: Overall Physical Activity Levels were assigned a grade of C-. Active Transportation and Sedentary Behaviors were both assigned a grade of C. Government Strategies and Investments was assigned a grade of D. Six other indicators, including the country-specific indicator Physical Fitness, were graded as INC (incomplete) due to the lack of nationally representative evidence. CONCLUSIONS: Based on existing evidence, it appears that most Indian children do not achieve recommended levels of physical activity and spend most of their day in sedentary pursuits. The report card identifies gaps in both investments and research that need to be addressed before understanding the complete picture of active living in children and youth in India.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".