Global Trends in Childhood Sexual Abuse and Bullying Victimization in 204 Countries: A Comprehensive Analysis From 1990 to 2019
Bibliographic record
Abstract
OBJECTIVES: No comprehensive analysis has yet been published regarding global trends in childhood sexual abuse (CSA) and bullying victimization (BV). The present study offers a longitudinal perspective on their prevalence worldwide. METHODS: CSA and BV rates were extracted from the Global Burden of Disease study, spanning the years 1990 to 2019 across 204 countries. Trends by gender, region, and human development index (HDI) were examined. RESULTS: For both boys and girls, and in both high-HDI and low-HDI countries, CSA rates did not significantly change from 1990 to 2019 (p>0.05). However, BV rates increased significantly in high-HDI and low-HDI countries for both genders (p<0.001). Subsequently, we analyzed trends separately by gender across all countries, without considering development level. In this analysis, CSA rates among girls decreased from 1990 to 2000, followed by an increasing tendency after 2000; overall, an upward trend was evident between 1990 and 2019 (p=0.029). In contrast, no significant pattern was observed for boys. Notably, BV demonstrated an increasing trend across all regions when HDI was not considered (p<0.05), with African populations experiencing the most pronounced rise (p<0.001). Globally, boys consistently exhibited higher BV rates than girls. CONCLUSIONS: Our research indicates that, on a global scale, rates of CSA among girls have been rising. Additionally, BV rates have increased in all regions for both boys and girls. Notably, this trend in BV rates is occurring irrespective of HDI. These findings underscore the necessity for targeted interventions in areas with high rates of CSA and BV.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".