Polyvictimization and psychopathology among children and adolescents: A systematic review of studies using the Juvenile Victimization Questionnaire
Bibliographic record
Abstract
BACKGROUND: Exposure to child abuse can lead to lasting mental health problems. Extant research has found that different types of child abuse tend to co-occur and overlap, which merits the investigation of the effects of exposure to multiple types of childhood mistreatment. OBJECTIVE: The aim of this study was to systematically review the evidence on the associations between multiple different types of interpersonal victimization or polyvictimization, and indicators of psychopathology among children ages 0-17. METHODS: The review included studies across all economic strata and research on nationally representative, community, and at-risk samples, using the same standardized assessment tool (i.e. the Juvenile Victimization Questionnaire or JVQ). The review was conducted using peer-reviewed evidence published up until August 2019 from Scopus, EMBASE, PsycINFO, Medline, CINAHL, and ERIC. Out of 4998 relevant references screened, 255 met the inclusion criteria, 22 of which aimed to address childhood polyvictimization and psychopathology. RESULTS: A total of 21 of the 22 included studies identified a significant positive association between polyvictimization and various indicators of psychopathology comprising both externalizing (e.g. anger), internalizing problems (e.g. depression) and total psychological distress. A range of studies demonstrated that polyvictimization was a stronger risk factor for psychopathology than individual (sub)types of victimization. Based on the study findings, we provide a set of recommendations for future research on polyvictimization and psychopathology. CONCLUSION: The present systematic review was the first to review the evidence on the associations between polyvictimization (as measured by the JVQ) and child and adolescent psychopathology in the global research literature. As a novel approach, the present review included both normative and high-risk samples. The results showed that polyvictimization is a substantial risk factor for mental health problems spanning both inner-directed and outer-directed mental health difficulties. However, the inconsistency in methods of defining and measuring polyvictimization severely undermines the scientific impact of this body of work. Additional well-designed, longitudinal studies that take account of the context-specific nature of polyvictimization are required to better establish the causal relationships between childhood polyvictimization and psychopathology so as to improve prevention and intervention efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".