Prevalence of Psychiatric Morbidity and Alcohol use Disorders Among Adolescent Indigenous Tribals from Three Indian States
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".