Global executive dysfunction, not core executive skills, mediate the relationship between adversity exposure and later health in undergraduate students
Bibliographic record
Abstract
Executive function (EF) represents a set of higher-order cognitive skills that permit engagement in goal-oriented behavior. EF deficits are associated with wide-ranging negative health-related consequences, including psychopathology and engagement in risky health-related behaviors. Because neural substrates supporting EF develop over a protracted period of time, an extended window of vulnerability exists whereby environmental stressors can interrupt development, culminating in lifelong EF deficits. We capitalized on this understanding of the vulnerability of EF-relevant neural structures to elucidate the link between adverse childhood experiences (ACEs) and early mortality. ACEs are highly prevalent in the general population and exert negative downstream implications for many health-related behaviors, ultimately hastening mortality. However, underlying mechanisms linking ACEs with poor health remain less understood. To address this gap in the literature, we assessed ACE history and health factors, including psychopathology and risky alcohol use behaviors in undergraduates. We further assessed EF using performance-based and rating scale measures. Results revealed that some measures of EF mediated the relationship between ACEs and current mental health, but EF did not mediate the association between ACEs and engagement in risky health-related behaviors. These results partially support a neurodevelopmental model of ACE exposure vis-à-vis future health, focusing on the role of EF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".