Prevalence and determinants of mental health problems among children in Mongolia: A population-based birth cohort
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Bibliographic record
Abstract
In Mongolia, more than one-third of adolescents and children may experience mental health problems, but high-quality population-based data are lacking. In this study, the authors investigated the prevalence and the correlates of mental health conditions among school-age children. The authors conducted a population-based birth cohort study with data collection between 2013 and 2016 in rural Bulgan, Mongolia. Mental health problems were assessed using the Strengths and Difficulties Questionnaire (SDQ-Mongolia). Protective and risk factors for mental health problems in six-year-old children were assessed using logistic regression with adjustment for potential confounders. A total of 1064 mother/child pairs participated, with a follow-up rate of 96.2%. Overall, 9.5% of children had abnormal emotional and behavioral scores (SDQ ≥17), rising to 20.1% when combined with borderline scores (SDQ ≥14). In the baseline analysis, smoking in family members (adjusted odds ratio [aOR] 1.58, 95% CI 1.05–2.38) was positively linked to child mental health problems. In the follow-up analyses when children were aged 6 year, maternal depression symptoms (aOR 1.66, 95% CI 1.13–2.44), smoking of family members (aOR 1.54, 95% CI 1.06–2.21), and maternal alcohol consumption (aOR 1.55, 95% CI 1.02–2·33) were associated with greater incidence of mental health problems, while storytelling (aOR 0.65, 95% CI 0.42–0.99) and hospital visits (aOR 0.56, 95% CI 0.38–0.79) demonstrated protective associations. Markers of low socioeconomic status were the most influential risk factors for children's mental health problems. Effective intervention toward family members' smoking, maternal depression and alcohol consumption, and increased attention to potentially protective factors, including storytelling and access to appropriate hospital care, may better support the mental health of children in Mongolia.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it