P03-05 State of the Evidence of Active Living among Children and Youth in India: A Scoping Review Informing the Global Matrix 4.0
Bibliographic record
Abstract
Abstract Background There is strong evidence of physical inactivity's link to global disease and economic burden. Physical inactivity among Indian children and youth has particular consequences for the global economy, as youth in India make up a substantial proportion of the world's workforce. As part of the 60-country Active Healthy Kids Alliance Global Matrix 4.0 initiative, a systematic scoping review was conducted to appraise the current state of evidence of active living among children and youth in India. Methods A systematic search of peer-reviewed and grey literature published over the last decade was conducted for 11 indicators of active living: overall physical activity, organized sport participation, active play, active transportation, sedentary behavior, family and peers, school programs and policies, community and built environment, government strategies, physical fitness, and yoga. Data sources included national, state (i.e., province) and city-level surveys, as well as primary data from ongoing longitudinal studies. Relevant grey literature, including government reports and school board policies, were also reviewed. Results Physical activity levels vary widely across India, with children and youth in rural settings accumulating greater moderate-to-vigorous activity and lower screen time compared to their urban counterparts. The majority of Indian children and youth report active transportation, however; they are not meeting recommended physical activity and sedentary behaviour guidelines. Despite the availability of many non-profit and organized programs, several indicators of active living including organized sports, active play, and yoga programming have not been evaluated for uptake or impact. Physical activity type and levels varied significantly across gender and socioeconomic status, with girls belonging to lower socioeconomic status having the greatest disadvantage due to cultural and safety perceptions. Conclusions While the vast majority of Indian children and youth are not accumulating recommended physical activity levels, there are encouraging signs of active transportation and active play?a phenomenon that needs to be further explored in India and other high-, middle- and low-income countries. The findings point to widespread disparities in access to active living resources and infrastructure between urban and rural settings. Targeted programs, policies, and resource allocation are necessary to improve built environment and safety for children and youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".