Leveraging translational neuroscience to inform early intervention and addiction prevention for children exposed to early life stress
Bibliographic record
Abstract
Substance use and addiction are disproportionately experienced by individuals with a history of exposure to early life stress (ELS), such as maltreatment, domestic violence and parent psychopathology. Unfortunately, extant interventions have mixed effectiveness at improving outcome trajectories for ELS-exposed children, who are often underserved by evidenced-based programs. Here, we employ a translational neuroscience framework to delineate how neuroscience can deepen our understanding of ELS-linked alterations in children's function to inform the development of more targeted, effective early intervention and addiction prevention programs. Candidate neural pathways altered by ELS and linked to addiction are described across sensory, affective, motivational, and executive function domains. Next, we provide an example of the application of translational neuroscience principles in a family of early interventions (i.e. Multidimensional Treatment Foster Care - Preschool, Kids in Transition to School) focused on improving self-regulation in ELS-exposed children. Future directions and areas of unmet need in intervention research detail the significant potential of translational neuroscience to advance interventionists' ability to support positive adjustment in ELS-exposed children and prevent harmful addiction outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".