Status, Hotspots, and Future Trends: Bibliometric Analysis of Research on the Impact of the Built Environment on Children and Adolescents’ Physical Activity
Bibliographic record
Abstract
Applying the visualized bibliometric analysis method, we explored the overall distribution characteristics, research progress, and hotspots of current research on the effect of the built environment on the physical activity of children and adolescents from 2003 to 2022. The research results indicate that the United States, Canada, Australia, and other Western countries are the primary forces of relevant research and have a solid foundation in the research on the impact of the built environment on the physical activity of children and adolescents. Sallis, Saelens, Gile-Corti, and other early authors have had a long-term, important role in this area. The research results have continuously guided new scientific research output for a long time, and emerging research forces have also played a directional role in future research trends, represented by publications such as American Preventive Medicine and Preventive Medicine. Obesity, health behaviors, home–school environment, and various correlations are the research hotspots in this field. This study systematically summarizes and analyzes research on the built environment’s promotion of physical activity among children and adolescents, and it provides valuable guidance and reference for follow-up research in the near future.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.119 | 0.192 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".