Modifiable predictors of depression following childhood maltreatment: a systematic review and meta-analysis
Bibliographic record
Abstract
Although maltreatment experiences in childhood increase the risk for depression, not all maltreated children become depressed. This review aims to systematically examine the existing literature to identify modifiable factors that increase vulnerability to, or act as a buffer against, depression, and could therefore inform the development of targeted interventions. Thirteen databases (including Medline, PsychINFO, SCOPUS) were searched (between 1984 and 2014) for prospective, longitudinal studies published in English that included at least 300 participants and assessed associations between childhood maltreatment and later depression. The study quality was assessed using an adapted Newcastle-Ottawa Scale checklist. Meta-analyses (random effects models) were performed on combined data to estimate the effect size of the association between maltreatment and depression. Meta-regressions were used to explore effects of study size and quality. We identified 22 eligible articles (N=12 210 participants), of which 6 examined potential modifiable predictors of depression following maltreatment. No more than two studies examined the same modifiable predictor; therefore, it was not possible to examine combined effects of modifiable predictors with meta-regression. It is thus difficult to draw firm conclusions from this study, but initial findings indicate that interpersonal relationships, cognitive vulnerabilities and behavioral difficulties may be modifiable predictors of depression following maltreatment. There is a lack of well-designed, prospective studies on modifiable predictors of depression following maltreatment. A small amount of initial research suggests that modifiable predictors of depression may be specific to maltreatment subtypes and gender. Corroboration and further investigation of causal mechanisms is required to identify novel targets for intervention, and to inform guidelines for the effective treatment of maltreated children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".