Mental health in the Americas: an overview of the treatment gap
Bibliographic record
Abstract
OBJECTIVE: To understand the mental health treatment gap in the Region of the Americas by examining the prevalence of mental health disorders, use of mental health services, and the global burden of disease. METHODS: Data from community-based surveys of mental disorders in Argentina, Brazil, Canada, Chile, Colombia, Guatemala, Mexico, Peru, and the United States were utilized. The World Mental Health Survey published data were used to estimate the treatment gap. For Canada, Chile, and Guatemala, the treatment gap was calculated from data files. The mean, median, and weighted treatment gap, and the 12-month prevalence by severity and category of mental disorder were estimated for the general adult, child-adolescent, and indigenous populations. Disability-adjusted Life Years and Years Lived with Disability were calculated from the Global Burden of Disease study. RESULTS: Mental and substance use disorders accounted for 10.5% of the global burden of disease in the Americas. The 12-month prevalence rate of severe mental disorders ranged from 2% - 10% across studies. The weighted mean treatment gap in the Americas for moderate to severe disorders was 65.7%; North America, 53.2%; Latin America, 74.7%; Mesoamerica, 78.7%; and South America, 73.1%. The treatment gap for severe mental disorders in children and adolescents was over 50%. One-third of the indigenous population in the United States and 80% in Latin America had not received treatment. CONCLUSION: The treatment gap for mental health remains a public health concern. A high proportion of adults, children, and indigenous individuals with serious mental illness remains untreated. The result is an elevated prevalence of mental disorders and global burden of disease.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".