Prevalence of child to parent violence and its determinants: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Child-to-parent violence (CPV) is an increasing issue globally, leading to serious adverse effects on families. This research aimed to conduct a systematic review and meta-analysis to assess the prevalence of CPV and identify its associated risk factors, thereby enhancing understanding of this escalating global issue. METHODS: In this systematic review and meta-analysis, a thorough search was carried out across various databases in English. The inclusion criteria were developed based on the SPIDER framework, targeting quantitative studies that focused on the prevalence and risk factors associated with CPV. Data were extracted systematically using a validated collection form, and the risk of bias was assessed using the Newcastle-Ottawa Scale. Statistical analyses included meta-regression and sensitivity analyses to determine the prevalence of CPV using Comprehensive Meta-Analysis version 3 software. Among the 25 included studies, 12 studies were included in the meta-analysis for prevalence, both overall and separately for violence against mothers and fathers. Additionally, 8 studies were analyzed through systematic and narrative data synthesis to examine determinants of CPV. RESULTS: The meta-analysis involving 25,000 participants revealed that the overall prevalence of CPV against both parents was 34.8%. Subgroup analyses indicated that physical violence prevalence was 10.0%, while psychological violence was significantly higher at 82.6%. Additionally, the descriptive analysis identified key determinants of CPV, including family conflict, parental stress, and adolescent behavioral issues, highlighting the complex interplay of factors contributing to this phenomenon and emphasizing the need for targeted interventions. CONCLUSION: The findings underscore the urgent need for targeted interventions that address these multifaceted issues to effectively reduce CPV and its impact on families. Future research should continue to explore these dynamics to inform effective prevention strategies.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".