Adverse childhood experiences and global mental health: avenues to reduce the burden of child and adolescent mental disorders
Bibliographic record
Abstract
Mental disorders are one of the largest contributors to the burden of disease globally, this holds also for children and adolescents, especially in low- and middle-income countries. The prevalence and severity of these disorders are influenced by social determinants, including exposure to adversity. When occurring early in life, these latter events are referred to as adverse childhood experiences (ACEs).In this editorial, we provide an overview of the literature on the role of ACEs as social determinants of mental health through the lenses of global mental health. While the relation between ACEs and mental health has been extensively explored, most research was centred in higher income contexts. We argue that findings from the realm of global mental health should be integrated into that of ACEs, e.g. through preventative and responsive psychosocial interventions for children, adolescents and their caregivers. The field of global mental health should also undertake active efforts to better address ACEs in its initiatives, all with the goal of reducing the burden of mental disorders among children and adolescents globally.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".