Developing and Validating a Model for Predicting Child and Adolescent Obesity in Greater Beirut, Lebanon: A Population and School-Based Study
Bibliographic record
Abstract
Objectives: The study aimed to identify predictors of child and adolescent obesity at the individual and school levels in Greater Beirut, Lebanon. Methods: This was a cross-sectional study using a representative sample of schoolchildren aged 8-12 years old residing in Greater Beirut, Lebanon (n=2,125). The study employed a two-stage sampling approach. The first stage randomly selected 50 schools stratified by type of school (public, private, and private free) from a list provided by the Ministry of Education in Lebanon. The second stage included randomly assigning 50 students from grades 4 to 6 from each school that participated in the study. Potential predictors of obesity at the individual and school levels were collected through a face-to-face survey during January to May 2022. The outcome was obesity, defined as a BMI Z-score > +2SD using the WHO growth standard. A LASSO logistic regression model was used to predict obesity. The model’s discrimination was measured through the area under the receiver operating characteristic curve (C-Statistic). The model’s calibration was measured through the calibration slope (C-Slope). Results: Almost 18% of the participants were obese. Individual-level obesity predictors included: being a male, being Lebanese compared to other nationalities, skipping breakfast on school days, and eating while watching television or other screen ≥ 3 days a week. Attending public school was the only school-level predictor retained. The model showed moderate discrimination of C-Statistic: 0.622 (95%CI:0.58-0.66) and had good calibration with a C-Slope of 1.04 (95%CI:0.70-1.38). Conclusions: The findings underline the ongoing need to address child and adolescent obesity in Greater Beirut by promoting healthy dietary habits focusing on breakfast consumption and limiting screen use during meals. It also emphasizes that community-level socioeconomic and structural factors play a role in schoolchildren’s obesity in Lebanon, and that interventions are needed in public schools to address the threats to healthy eating among schoolchildren. Funding Sources: International Development Research Centre - Canada.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".