Assessment of Abuses of Children with Disabilities in Japanese Nursery Schools by Municipality
Bibliographic record
Abstract
Introduction: Many children with disabilities and latent characteristics are cared for in nursery schools (daycare centres). Paediatricians commissioned by nursery schools in our previous survey proved the occurrence of disabled children abused in schools. As the parents of the alleged victims were likely to contact the municipality, a survey of the municipalities managing childcare was requested. Methods: The target group was local authorities' childcare service management departments (public authorities). Questionnaires were sent twice to 1,742 municipalities from late 2012 to the fiscal year (FY) 2014. Incidents were collected by inquiring about implementing preventive measures and reasonable accommodations. Results: The number of municipalities that responded was 490 and 595, respectively, and 38 and 7 municipalities received complaints from parents that instructors had abused their children with disabilities. Based on the number of reports, the incidence of abuse in nursery schools in 2014 was estimated to be 0.0077% (95% CI: 0.0059–0.0094) and 0.0208% (0.0073–0.0344) for children with disabilities. In the 2014-2015 research periods, more teachers and staff were made aware of the consultation service as a preventive measure, and more emphasis was placed on the informed consent of parents as a reasonable accommodation. Regional characteristics, classified by the percentage of children, influenced the implementation of some measures. Conclusion: The incidence in Japan was estimated to be lower than in other countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".