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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".