Prevention Strategies in Obesity Management: A Systematic Review Comparing Canadian and American Guidelines for Adults
Bibliographic record
Abstract
The fast-increasing obesity prevalence rates in children, youths, and adults in the last decade have made obesity prevention a global public health priority. The primary objective of this study is to evaluate the various obesity prevention strategies and guidelines implemented in the United States and Canada. Thus, for this study, a systematic review was performed on various online databases including PubMed, Scopus, Google Scholar, and MEDLINE. The decision to study the obesity prevention strategies in Canada and the United States is a result of the high prevalence rates of obesity in the two countries, alongside the numerous prevention interventions that have been executed to prevent obesity. Additionally, the systematic review used robust methodology that followed the Cochrane guidance and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published between 2014 and 2024, drawn from listed databases, were included in this systematic review. The quality of the included studies was evaluated using the appraisal tool for cross-sectional studies, with the studies being rated moderate to high quality. Therefore, a total of 15 studies met the inclusion criteria and were reviewed. The findings indicate that various obesity prevention interventions have been implemented across the United States and Canada, with diverse degrees of success in obesity prevention and management. Food labeling, regular exercises, portion size regulation, school-based intervention strategies, early childhood Intervention programs, and sugar-sweetened beverage taxation were found to be effective interventions for preventing obesity in children and adults. Based on the findings, there is a need to ensure full execution of the different interventions to ensure significant reduction in obesity prevalence, as well as prevention of obesity in different populations.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".