The prevalence of obesity among school-aged children in Vietnam: A systematic review and meta-analysis
Bibliographic record
Abstract
The global incidence of obesity is rising, posing a substantial public health threat. This meta-analysis aim to estimate of the prevalence of obesity among school-aged children in Vietnam and to analyze the risk variables that have been linked to this problem. MEDLINE, PubMed, and Scopus were used to identify articles published up to May 2022. According to peer-reviewed literature, studies reported the proportion of obesity among Vietnamese school-aged children. The Scales of Newcastle-Ottawa Quality Assessment was used to evaluate the study quality for all qualifying research. The data was analyzed using R-Studio software, and the combined effects were estimated using a random-effects model. Cochran's Q-test and the I2 test were employed to examine heterogeneity. Egger's test was used to determine publication bias. Eleven studies with 27,363 participants were suitable for inclusion in the final model after meeting the prerequisites. The proportion of obesity among Vietnamese school-aged children was 13.08% (95% CI, 7.04%–23.01%) with higher heterogeneity through the observed prevalence estimates (Q = 1,0339, p < 0.01, I2 = 99%). A higher prevalence was observed in boys (17.5%) than in girls (8.07%). Male gender of the children: 2.42 (95%CI: 1.43–4.09), mothers have less education: 2.63 (95% CI 1.52–4.55) have shown a positive association with the development of obesity among children. The recent pooled analysis of studies demonstrates that school-aged children in Vietnam have a high prevalence of obesity. The male gender and the low education status of the mother were found to be significantly associated with obesity. The findings provide evidence for prevention intervention strategies to reduce obesity in school-age children.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".