The effects of early life adversity on children’s mental health and cognitive functioning
Bibliographic record
Abstract
Emerging evidence suggests that partially distinct mechanisms may underlie the association between different dimensions of early life adversity (ELA) and psychopathology in children and adolescents. While there is minimal evidence that different types of ELA are associated with specific psychopathology outcomes, there are partially unique cognitive and socioemotional consequences of specific dimensions of ELA that increase transdiagnostic risk of mental health problems across the internalizing and externalizing spectra. The current review provides an overview of recent findings examining the cognitive (e.g., language, executive function), socioemotional (e.g., attention bias, emotion regulation), and mental health correlates of ELA along the dimensions of threat/harshness, deprivation, and unpredictability. We underscore similarities and differences in the mechanisms connecting different dimensions of ELA to particular mental health outcomes, and identify gaps and future directions that may help to clarify inconsistencies in the literature. This review focuses on childhood and adolescence, periods of exquisite neurobiological change and sensitivity to the environment. The utility of dimensional models of ELA in better understanding the mechanistic pathways towards the expression of psychopathology is discussed, with the review supporting the value of such models in better understanding the developmental sequelae associated with ELA. Integration of dimensional models of ELA with existing models focused on psychiatric classification and biobehavioral mechanisms may advance our understanding of the etiology, phenomenology, and treatment of mental health difficulties in children and youth.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".