Child Abuse in Indonesia: A National Trend Analysis and Health Promotion Response (2016-2024)
Bibliographic record
Abstract
Child abuse remains a critical issue in Indonesia. Cases have been tracked in real time through the Online Information System for the Protection of Women and Children ( SIMFONI-PPA ) since 2016; however, long-term trends have not been comprehensively analysed. This study examined national child abuse trends in Indonesia and explored health promotion strategies for prevention. Secondary data from SIMFONI-PPA were analysed to identify trends in abuse types, victim profiles, perpetrator profiles, and locations. A literature review was conducted to identify prevention strategies employing a health promotion framework grounded in the Ottawa Charter. Findings showed a steady increase in reported child abuse cases. Sexual violence was the most reported abuse, 2–3 times higher than physical or psychological violence and several times more than trafficking or exploitation. Adolescent girls (approximately 70% of victims), aged 13–17 (over 50% victims), were the most affected; about one-third of victims were attending junior high school. Perpetrators were known to the victims, such as lovers or peers (about a quarter of cases), and most incidents occurred at home (around half of reported cases). Indonesia has implemented several prevention strategies, including policy reform, education, and community-based efforts. The rising trend of child abuse in Indonesia underscores the urgent need for multisectoral, context-sensitive interventions focused on improving data collection, service access, and reducing stigma. Future research should explore socio-cultural factors and evaluate the effectiveness of existing prevention programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".