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An Assessment of Abuse of Children with Disabilities at Japanese Nursery Schools: Reports by Commissioned Pediatricians

2022· article· en· W4210845841 on OpenAlexvenueno aff
Toshihiro Horiguchi, Kenji Takanashi, Shôichi Sato, Naoki Sone

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2022
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsPre schoolComplaintMedicineFamily medicinePediatricsGrievanceChild abusePsychologySuicide preventionPoison controlMedical emergencyDevelopmental psychologyPolitical science

Abstract

fetched live from OpenAlex

Background: In a preschool setting, childcare is usually provided to children with disabilities or potential features as well. In this study, we hypothesized that parents might have additionally consulted commissioned pediatricians when their children were reportedly abused by schoolteachers as well as following legitimate grievance procedures. Aims: To estimate the prevalence rate of children with disabilities being abused at nursery schools in Japan, the authors asked the help of commissioned pediatricians of nursery schools. Methods: We sent a questionnaire to 1,607 members of the Japanese Society for Well-being of Nursery-schoolers, who are specialists known to be highly sensitive to abuses. Incident reports from the second half of 2012 to the fiscal year 2014 were collected from 361 members who replied. Results: In total, three respondents, including one pediatrician, had received a complaint(s) from parents that their child with disabilities had been abused by a teacher. No details on each case were provided. Conclusion: Thus, the need for another assessment to administrative was indicated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.371
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueJournal of Intellectual Disability - Diagnosis and TreatmentSame topicFamily and Disability Support ResearchFrench-language works237,207