Bibliographic record
Abstract
Background: To combat the global health crisis of obesity, many interventions have been implemented, including in children and adolescents. This age range is uniquely important because health behavior continues into adulthood, resulting in lifelong health risks or benefits. This narrative review aims to provide a cross section of the scientific literature regarding obesity interventions by setting, including school-based, daycare-based, home-based, healthcare-based, and digital-based, as well as to highlight gaps in research. Methods: Articles written in English addressing childhood and adolescent obesity interventions were sought online using PubMed and Google Scholar searches. Although some articles were from a global perspective, the majority focused on children in the United States. This search included reviews, individual studies, and other related papers. Results: School-based interventions are accessible to many, but there is limited evidence of long-term benefits. Home-based interventions were the only setting to have compelling evidence of long-term benefits, although there are several barriers to participation. Healthcare-based interventions are often successful when specific strategies and unique advantages of healthcare settings are utilized. Digital interventions have limited success now, but show potential for cost-effective scaling up as technology improves. Conclusion: The clearest gap in research is the lack of long-term studies, especially of school-based and healthcare-based interventions. Thus, it is imperative that investments are made into studies that include follow-up components continuing at least 1-2 years after the intervention. Additionally, home-based interventions have been more successful during early childhood while school-based interventions tend to be more successful during adolescence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 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".