Prevalence of obesity and associated sociodemographic and lifestyle factors in Morocco
Bibliographic record
Abstract
OBJECTIVE: In Morocco, the association between obesity/overweight and socio-demographic and lifestyle factors is poorly understood. The present study aimed to investigate this association in a representative sample of the Moroccan population aged 18 years and above. DESIGN: This is a cross-sectional study using a questionnaire including demographic, socio-economic and physical activity items. Height and weight were measured and BMI was computed. The association between obesity (BMI ≥ 30.0 kg/m2) or overweight (25.0 ≤ BMI < 29.9 kg/m2) and the other variables was analysed using multiple binomial logistic regression, separately in men and women. SETTING: The whole Moroccan territory. SUBJECTS: A total of 2891 subjects took part in the survey (1430 men and 1461 women). RESULTS: The prevalence of obesity was 20.9 % in women and 6.0 % in men (P < 0.0001). The prevalence of overweight was 32.9 % in women v. 26.8 % in men (P < 0.0001). In women, the risk of obesity and overweight increased with age, with the highest risk being in individuals aged 45-54 years (OR = 3.02, 95 % CI 2.06, 4.44) compared to individuals <35 years old. Married women were more prone to obesity and overweight (OR = 2.42, 95 % CI 1.50, 3.91) than single women. In men, the risk of obesity and overweight increased with average family income (OR = 2.62, 95 % CI 1.40, 4.87 for family income ≥5000 MAD/month compared to <2000 MAD/month) and in married persons (OR = 3.75, 95 % CI 1.78, 7.81) compared to single individuals. CONCLUSIONS: These results contribute to target groups in whom prevention programmes could be implemented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".