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Record W2099398601 · doi:10.1177/1077559510388843

Child Maltreatment Investigations Involving Parents With Cognitive Impairments in Canada

2010· article· en· W2099398601 on OpenAlexaffabout
David McConnell, Maurice A. Feldman, Marjorie Aunos, Narasimha Prasad

Bibliographic record

VenueChild Maltreatment · 2010
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsQuebec Rehabilitation Research NetworkBrock UniversityUniversity of Alberta
Fundersnot available
KeywordsNeglectCognitionReferralPsychologyChild abusePoison controlInjury preventionSuicide preventionClinical psychologyHuman factors and ergonomicsChild neglectDevelopmental psychologyLogistic regressionPsychiatryMedicineMedical emergencyFamily medicine

Abstract

fetched live from OpenAlex

The authors examined decision making and service referral in child maltreatment investigations involving children of parents with cognitive impairments using the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS-2003) core-data. The CIS-2003 includes process and outcome data on a total of 1,243 child investigations (n = 1,170 weighted) in which parental cognitive impairment was noted. Employing binary logistic regression analyses, the authors found that perceived parent noncooperation was the most potent predictor of court application. Alternative dispute resolution was rarely utilized. The findings from this study highlight the need for development and utilization of alternative dispute resolution strategies, worker training, dissemination of evidence-based parent training programs, and implementation of strategies to alleviate poverty and strengthen the social relationships of parents with cognitive impairments and promote a healthy start to life for their children.

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)0.013
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 score0.028
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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

Citations58
Published2010
Admission routes2
Has abstractyes

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