Early childhood trauma and its long-term impact on cognitive and emotional development: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Childhood trauma has profound, long-term effects on cognitive and emotional development. This systematic review and meta-analysis sought to synthesis the evidence around the long-term impact of human childhood trauma on domains of cognition and emotion in order to inform interventions and public health strategies. METHODS: We systematically reviewed 465 studies from PubMed, Cochrane Library, and Google Scholar, 9 studies were included after duplicates were removed and inclusion and exclusion criteria were applied, and all 9 studies were aimed at low-income people in the United States. Data on study design, trauma types, and cognitive/emotional outcomes were extracted. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) and Cochrane Risk of Bias Tool (RoB 2). Random-effects meta-analysis and subgroup analyses (processing speed, attention, working memory, emotion regulation, executive function) were conducted using R software. RESULTS: : 77-98%) reflected variability in trauma types and assessments. The pooled effect size (SMD = 1.57, 95% CI: [-0.12, 3.26]) highlighted trauma's pervasive impact. CONCLUSION: Childhood trauma disproportionately impairs attention and working memory. These findings, however, point to the importance of early screening, trauma-informed care and targeted interventions to ameliorate the long-term consequences of trauma, even with high heterogeneity. Methodological variability should be addressed to inform prevention and treatment strategies in future research, as well as resilience factors explored.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".