Diffusion of efficacious interventions for children and adolescents with mental health problems
Bibliographic record
Abstract
BACKGROUND: Child and adolescent mental health problems are prevalent and require adequate interventions. Despite several evidence-based interventions for these problems described in the literature, few studies addressed strategies to diffuse efficacious interventions for child mental disorders especially in developing countries. METHODS: An extensive but not systematic review of the literature was performed aiming to identify evidence-based interventions for children and adolescents with mental disorders, professionals to target in disseminating these interventions, and the available strategies to diffuse information. RESULTS: Substantial evidence-based information is available to guide preventive, psychosocial and psychopharmacological interventions. The effectiveness of treatments in real-world settings and the cost-effectiveness of interventions are rarely tested. Professionals at different levels (teachers, school counselors, social workers, general practitioners, pediatricians, child psychologists and psychiatrists) should be targeted in diffusing efficacious interventions for child mental health problems worldwide. Telepsychiatry and the internet seem to be the most promising strategies to diffuse knowledge with lower costs. CONCLUSIONS: Medical and allied professionals must incorporate child and adolescent mental health issues in their under- and postgraduate curricula, and be better prepared to critically evaluate available information. Professionals need to disseminate evidence-based programs to guide parents and teachers in developing countries to deal with child and adolescent difficulties. Countries need to explore internet solutions for dissemination of medical information.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".