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Record W3020093488 · doi:10.1080/15548732.2020.1751770

The intersection of child welfare, intimate partner violence and child custody disputes: secondary data analysis of the Ontario incidence study of reported child abuse and neglect

2020· article· en· W3020093488 on OpenAlexafffundabout
Tara Black, Michael Saini, Barbara Fallon, Sevil Deljavan, Ricardo Theoduloz

Bibliographic record

VenueJournal of Public Child Welfare · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Toronto
FundersCanada Research Chairs
KeywordsNeglectChild abuseWelfareDomestic violencePsychologyChild neglectChild protectionChild custodyPoison controlLogistic regressionCriminologySuicide preventionPsychiatryMedicinePolitical scienceEnvironmental healthLawNursing

Abstract

fetched live from OpenAlex

Purpose: The purpose of this paper is to understand child welfare investigations that involve child custody disputes in Ontario, Canada. Methods: The study used data from the Ontario Incidence Study of Reported Child Abuse and Neglect. The OIS is a cyclical, cross sectional provincial child welfare study conducted every five years. Characteristics of investigations involving child custody disputes were examined by conducting chi-square tests on key variables, and a logistic regression was performed to examine the influence of child custody disputes on transfers to ongoing services. Findings: In 2013 approximately 12% of child welfare investigations involved a child custody dispute. These investigations were predominantly referred by a custodial parent, and the primary maltreatment concern was exposure to emotional violence. Investigations involving custody disputes when controlling for all other predictors of maltreatment, were less likely to be transferred to ongoing child welfare services. Implications: There is a complicated relationship between child custody disputes and investigations involving intimate partner violence. Policy and practice implications are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.295
Teacher spread0.265 · 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 teacher head, not a consensus.

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

Citations11
Published2020
Admission routes3
Has abstractyes

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