The science of child and adolescent mental health in Brazil: a nationwide systematic review and compendium of evidence-based resources
Bibliographic record
Abstract
BACKGROUND: Brazil is home to 50 million children and adolescents, whose mental health needs require context-sensitive research. Although scientific output is growing in the country, publications remain scattered and often inaccessible. METHODS: This systematic review compiles prevalence estimates, assessment instruments and interventions for child and adolescent mental health-related outcomes in Brazil (PROSPERO: CRD42023491393). We searched international (PubMed, Web of Science, PsycINFO, Google Scholar) and regional (Scielo, Lilacs, Brazilian Digital Library of Theses and Dissertations) databases up to July 2024. We consulted reference lists and experts. Extraction followed Consensus-based Standards for the selection of health Measurement Instruments and Cochrane manuals. RESULTS: We included 734 studies on 2576 prevalence estimates, 908 studies on 912 instruments and 192 studies on 173 intervention trials. The prevalence of any mental disorder ranged from 10.8% (12-year-olds; Pelotas, RS) to 19.9% (ages 7-14; Porto Alegre, RS and São Paulo, SP) although a nationally representative study is lacking. There is an alarming rise in self-harm notifications, reaching 133 in 2019 (per 100 000 aged 10-19). Indigenous youth face suicide rates of 11 (ages 10-14), far exceeding national numbers (0.652). Nationwide surveys reveal severe violence exposure (eg, 21% of adolescents suffer physical violence at home in the previous year), disproportionately impacting Black youth and increasing risk for mental disorders. There are reliable instruments for assessing psychopathology, yet most lack cross-cultural validation. Interventions remain underimplemented. The largest trials adapted substance-use prevention programmes from high-income countries, proving ineffective in Brazil. Public investment is the primary driver of research, which is centralised in wealthier states and misrepresents social minorities. DISCUSSION: This review provides timely access to appraised evidence-based resources, facilitating uptake into practice. Brazil's historical sociocultural challenges impact youth mental health, with public health priorities including systemic violence, racism and indigenous suicide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.037 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".