Prevalence of Psychiatric Morbidity and Alcohol use Disorders Among Adolescent Indigenous Tribals from Three Indian States
Why this work is in the frame
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Bibliographic record
Abstract
Background: Among the Indian adolescents, the prevalence of psychiatric morbidity and alcohol use disorders (AUD) are 7.3% and 1.3%. However, no separate data are available for indigenous tribal populations. This study estimated the prevalence of psychiatric morbidity and AUD and associated socio-demographic factors among adolescents in the tribal communities in three widely varying states in India. Methods: Using validated Indian versions of the MINI 6.0, MINI Kid 6.0, and ICD-10 criteria, we conducted a cross-sectional survey from January to May 2019 in three Indian sites: Valsad, Gujarat (western India); Nilgiris, Tamil Nadu (south India); and East Khasi Hills district of Meghalaya (north-east India) on 623 indigenous tribal adolescents. Results: Aggregate prevalence of any psychiatric morbidity was 15.9% (95% CI: 13.1–19.0) (males: 13.6%, 95% CI: 10.0–18.1; females: 17.9%, 95% CI: 13.9–22.6), with site-wise statistically significant differences: Gujarat: 23.8% (95% CI: 18.1–30.2), Meghalaya: 17.1% (95% CI: 12.4–22.7), Tamil Nadu: 6.2% (95% CI: 3.2–10.5). The prevalence of diagnostic groups was mood disorders 6.4% ( n = 40), neurotic- and stress-related disorders 9.1% ( n = 57), phobic anxiety disorder 6.3% ( n = 39), AUD 2.7% ( n = 17), behavioral and emotional disorders 2.7% ( n = 17), and obsessive-compulsive disorder 2.2% ( n = 14). These differed across the sites. Conclusion: The prevalence of psychiatric morbidity in adolescent tribals is approximately twice the national average. The most common psychiatric morbidities reported are mood (affective) disorders, neurotic- and stress-related disorders, phobic anxiety disorder, AUD, behavioral and emotional disorders, andobsessive-compulsive disorder.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it