The COVID‐19 Pandemic and the Prevalence of Childhood Obesity in Turkiye
Bibliographic record
Abstract
OBJECTIVES: To examine the prevalence of overweight and obesity in school-age children in Isparta Province, Turkiye, during the coronavirus disease 2019 (COVID-19) pandemic, and to compare the results with those of previous studies conducted in Isparta in 2005, 2009, and 2014. METHODS: The study was carried out in schools in the city center of Isparta in March 2022, during which the weight and height of students were measured using a scale and stadiometer, and the body mass indices (BMI), BMI percentiles, and z scores were calculated. RESULTS: Of the 8871 students assessed, 4547 (51.3%) were female and the mean age of the sample was 11.92 ± 3.42 (6-18.93) years. The prevalence of overweight was 12%, the prevalence of obesity was 14.5%, and the prevalence of overweight + obesity was 26.5%. A comparison of the figures since 2005 revealed the prevalence of overweight to be stable, while the prevalence of obesity and the prevalence of overweight + obesity were found to have witnessed a increase (χ 2 : 57.01, P < 0.001). The prevalence of obesity was 13% among girls under 11 years of age and 14.3% among girls over 11 years of age; and 18.2% and 12.8% among boys under and over 11 years of age, respectively (χ 2 : 23.26, P < 0.001). The prevalence of obesity was significantly higher in boys under 11 years of age. CONCLUSIONS: The prevalence of overweight + obesity was nearly stable in the 3 studies conducted over the 17-year period, but we witnessed an increase in our most recent study conducted during the pandemic. The prevalence of obesity is significantly higher in boys under 11 years of age.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".