The Timing Effect of Childhood Maltreatment in Depression: A Systematic Review and meta-Analysis
Bibliographic record
Abstract
Although empirical evidence has confirmed the causal relationship between childhood maltreatment and depression, findings are inconsistent on the magnitude of the effect of age of exposure to childhood maltreatment on psychological development. This systematic review with meta-analysis aims to comprehensively synthesize the literature on the relationship between exposure age of maltreatment and depression and to quantitatively compare the magnitude of effect sizes across exposure age groups. Electronic databases and grey literature up to April 6th, 2022, were searched for English-language studies. Studies were included if they: 1) provided the information on exposure age; and 2) provided statistical indicators to examine the relationship between childhood maltreatment and depression. Fifty-eight articles met eligibility criteria and were included in meta-analyses. Subgroup analyses were conducted based on subtypes of maltreatment and measurements of depression. Any kind of maltreatment (correlation coefficient [r] = 0.17, 95% CI = 0.15–0.18), physical abuse (r =0.13, 95% CI = 0.10–0.15), sexual abuse (r = 0.18, 95% CI = 0.15–0.21), emotional abuse (r = 0.17, 95% CI=0.11–0.23), and neglect (r = 0.08, 95% CI=0.06–0.11) were associated with an increased risk of depression. Significant differential effects of maltreatment in depression were found across age groups of exposure to maltreatment (Q = 34.81, p < 0.001). Age of exposure in middle childhood (6–13 years) had the highest risk of depression, followed by late childhood (12–19 years) and early childhood (0–6 years). Implications of the findings provide robust evidence to support targeting victimized children of all ages and paying closer attention to those in middle childhood to effectively reduce the risk of depression.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".