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Record W3031508710 · doi:10.1177/1077559520923757

Young Children and Ongoing Child Welfare Services: A Multilevel Examination of Clinical and Worker Characteristics

2020· article· en· W3031508710 on OpenAlexaffabout
Joanne Filippelli, Kristen Lwin, Barbara Fallon, Nico Trocmé

Bibliographic record

VenueChild Maltreatment · 2020
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsMcGill UniversityUniversity of WindsorUniversity of Toronto
Fundersnot available
KeywordsWelfareNeglectPoison controlChild abuseHuman factors and ergonomicsOccupational safety and healthSuicide preventionInjury preventionMedicinePsychologyDevelopmental psychologyNursingEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

There is a growing body of research that underscores that young child welfare-involved children are a unique vulnerable subgroup of children. The decision to provide postinvestigation child welfare services is consequential to children's safety and well-being and has fiscal implications for organizations. Despite the potential ramifications of the decision, there is little known about the factors associated with the ongoing services provision for young children. This study uses secondary data analysis of the Canadian Incidence Study of Reported Child Abuse and Neglect 2008 to explore what case and worker factors predict the provision of ongoing child welfare services. Multilevel modeling was used to assess the relationship between independent variables and the decision to provide ongoing services; analyses included 2,296 children and 555 workers. Case and worker characteristics, including worker training and worker position, predicted ongoing child welfare services suggesting that further research examining the role of what worker characteristics impact child welfare decisions is warranted and essential.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.212
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.300
Teacher spread0.271 · 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.

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

Citations7
Published2020
Admission routes2
Has abstractyes

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