Treated prevalence of and mental health services received by children and adolescents in 42 low‐and‐middle‐income countries
Bibliographic record
Abstract
BACKGROUND: Little is known about the treated prevalence and services received by children and adolescents in low- and middle-income countries (LAMICs). The purpose of this study is to describe the characteristics and capacity of mental health services for children and adolescents in 42 LAMICs. METHODS: The World Health Organization Assessment Instrument for Mental Health Systems (WHO-AIMS), a 155-indicator instrument developed to assess key components of mental health service systems, was used to describe mental health services in 13 low, 24 lower-middle, and 5 upper-middle-income countries. Child and adolescent service indicators used in the analysis were drawn from Domains 2 (mental health services), 4 (human resources), and 5 (links with other sectors) of the WHO-AIMS instrument. RESULTS: The median one-year treated prevalence for children and adolescents is 159 per 100,000 population compared to a treated prevalence of 664 per 100,000 for the adult population. Children and adolescents make up 12% of the patient population in mental health outpatient facilities and less than 6% in all other types of mental health facilities. Less than 1% of beds in inpatient facilities are reserved for children and adolescents. Training provided for mental health professionals on child and adolescent mental health is minimal, with less than 1% receiving refresher training. Most countries (76%) organize educational campaigns on child and adolescent mental health. CONCLUSIONS: Mental health services for children and adolescents in low- and middle-income countries are extremely scarce and greatly limit access to appropriate care. Scaling up of services resources will be necessary in order to meet the objectives of the WHO Mental Health Gap Action (mhGAP) program which identifies increased services for the treatment of child mental disorders as a priority.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".