Prevalence and correlates of adverse childhood experiences in fathers: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Adverse childhood experiences (ACEs) are an important precursor for psychological and physical illness, with important implications for intergenerational risk. However, most of the literature on the intergenerational transmission of ACEs and related risks has focused on maternal ACEs and their impact on prenatal and postnatal health and parenting practices. Given that men are as likely as women to experience ACEs, and that fathers' involvement in childrearing has grown significantly in recent decades, it is important to understand the prevalence and intergenerational correlates of paternal ACEs. OBJECTIVE: This systematic review aimed to synthesize existing research on paternal ACEs. METHODS: A systematic review was conducted in CINAHL, EMBASE, Medline, and PsycINFO, identifying 66 studies (drawn from 56 distinct samples) reporting on fathers' ACEs separately from mothers' ACEs. Data on the prevalence and correlates of paternal ACEs were extracted from each study. RESULTS: Contrary to population-based studies comparing men and women, a significant difference emerged between fathers' and mothers' ACEs, with mothers presenting higher ACEs (Hedge's g = 0.18, 95 % CI [0.12, 0.25], p < .001). The most reported ACEs among fathers were parental separation/divorce, followed by psychological violence and neglect. The literature on correlates remains sparse, although it suggests associations between paternal ACEs and paternal depression, as well as child ACEs and behavioral problems. CONCLUSIONS: There is an urgent need for more research on paternal ACEs to help inform targeted prevention and intervention efforts with fathers. An adapted version of the Heuristic model of fathering is proposed to guide 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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".