Examining adverse childhood experiences and attention deficit/hyperactivity disorder: A systematic review
Bibliographic record
Abstract
Abstract Adverse childhood experiences (ACEs) comprise many dimensions of abuse and neglect in early development. Attention deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder that often begins in childhood. In this review, we investigated the associations between ACEs and ADHD in children. Specifically, the focus is to determine the extent of the relationship between ACE type, cumulative number, and ADHD severity. Furthermore, this study explored all aspects of the bidirectional nature of this relationship including how children with ADHD may experience greater ACEs and the potential contribution of confounding and mediating variables including comorbid conditions and resilience. Selected studies were published between January 2015 and January 2023 on PsychInfo, Google Scholar, PubMed, and Scopus. Selected studies included: (1) The main age group of the study was children; (2) The children had to have been diagnosed with or have parent‐reported ADHD; and (3) The research must include ACE. Case studies and those not meeting the inclusion criteria were excluded from this review. Ultimately, 43 studies met the inclusion criteria, were included in this review, and were evaluated using the appropriate risk of bias assessment tools. These studies supported a positive association between ACEs and ADHD including cumulative quantity and select types of ACEs increasing ADHD severity. Previous literature has primarily utilized observational methodologies which prevent researchers from establishing if there are causal associations and if there is a temporal order to ACEs and ADHD development. This review also provides implications for future research.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".