Diagnosis and Treatment of Obesity among Mexican Adults
Bibliographic record
Abstract
OBJECTIVES: To quantify the access to diagnosis and treatment of obesity and intentional weight loss among obese adults in Mexico and to identify the sociodemographic factors related to these events. METHODS: The 2006 Mexican National Health and Nutrition Survey - representative of the adults aged 20 to 64 years - was analyzed. Whether people had received diagnosis and treatment from health professionals and whether they had intentional weight loss were explored. The independent variables were: sex, age, socioeconomic position, locality size, and body weight perception. Analyses were carried out for obese people only (BMI ≥ 30 kg/m(2), N = 8,545). RESULTS: Among obese people, just 20.2% were diagnosed with such condition, only 8.0% undertook treatment, and barely 5.6% had lost weight intentionally. Individuals with a higher BMI, older individuals, people with higher education, those living in wealthier households, and those living in metropolitan areas were more likely to receive diagnosis and treatment for obesity. Women and people who had been diagnosed as obese were more likely to lose weight. CONCLUSION: There is an urgent need to increase access to diagnosis and treatment of obesity in Mexico, particularly for men and for lower socioeconomic groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".