Collaborative Supervision Strategy To Prevent Child Violence In Temporary Care Institutions
Bibliographic record
Abstract
Child abuse remains a serious issue in Child Care Centers, which serve as temporary care institutions for children of working parents—particularly mothers who are the main breadwinners. Several recent cases, including the death of an infant at Princess Childcare Bali, physical abuse in a Serpong daycare, and neglect at facilities such as Early Step in Pekan and Wensen School Indonesia in Depok, highlight the vulnerability of children in these settings. These incidents negatively affect children's growth and development and indicate systemic weaknesses in supervision and regulation. Data reveal that 44% of daycare centers operate without legal permits, 96% provide care services, yet only 33.3% of caregivers are certified. This reflects inadequate enforcement of care standards and fragmented oversight due to unclear inter-sectoral roles and responsibilities. This study uses a qualitative approach with a Systematic Literature Review (SLR) methodology to explore the current gaps in policy and institutional practice. The objective is to develop actionable policy recommendations that strengthen child protection in temporary care institutions. The findings point to a critical need for integrated, cross-sectoral supervision mechanisms and standardized caregiver certification requirements. The main policy recommendation is to revise and enhance the Child-Friendly District/City Evaluation Indicators to include clearer guidelines on childcare service regulation, oversight coordination, and caregiver competency standards. These measures are essential to improving institutional accountability and ensuring child safety and well-being in childcare facilities.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".