Disentangling the Associations Between Attention Deficit Hyperactivity Disorder and Child Sexual Abuse: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: An association between child sexual abuse (CSA) and attention deficit hyperactivity disorder (ADHD) has been documented. However, the temporal relationship between these problems and the roles of trauma-related symptoms or other forms of maltreatment remain unclear. This review aims to synthesize available research on CSA and ADHD, assess the methodological quality of the available research, and recommend future areas of inquiry. METHODS: Studies were searched in five databases including Medline and PsycINFO. Following a title and abstract screening, 151 full texts were reviewed and 28 were included. Inclusion criteria were sexual abuse occurred before 18 years old, published quantitative studies documenting at least a bivariate association between CSA and ADHD, and published in the past 5 years for dissertations/theses, in French or English. The methodological quality of studies was systematically assessed. RESULTS: Most studies identified a significant association between CSA and ADHD; most studies conceptualized CSA as a precursor of ADHD, but only one study had a longitudinal design. The quality of the studies varied greatly with main limitations being the lack of (i) longitudinal designs, (ii) rigorous multimethod/ multiinformant assessments of CSA and ADHD, and (iii) control for two major confounders: trauma-related symptoms and other forms of child maltreatment. DISCUSSION: Given the lack of longitudinal studies, the directionality of the association remains unclear. The confounding role of other maltreatment forms and trauma-related symptoms also remains mostly unaddressed. Rigorous studies are needed to untangle the association between CSA and ADHD.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".