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Record W2596115861 · doi:10.25071/ryr.v1i0.40344

Briefing Notes: The Prevalence of Mental Health Issues in Children and Youth Involved with Child Welfare Services in Ontario

2014· article· en· W2596115861 on OpenAlexaboutno aff
Patricia Ki

Bibliographic record

VenueRevue YOUR Review (York Online Undergraduate Research) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthWelfarePovertyAgency (philosophy)Stigma (botany)Social WelfareMedicinePsychiatryPsychologyEconomic growthPolitical scienceSociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

This paper examines the prevalence of mental health issues among children and youth served by child welfare services in Ontario and reviews academic literature, agency reports, and newspaper articles on the topic. The prevalence of mental health issues among young people involved with child welfare services is due not only to individual and family difficulties, but also to structural issues within the service sectors. Statistics illustrate a severe lack of accessible and timely services in both the child welfare and the children’s mental health sectors. Inadequate access to services for young people involved in the child welfare system is considered in relation to neoliberal policy development, social stigma, the Children and Family Services Act, and the experiences reported by families and service providers. Involvement with child welfare agencies, mental health, and structural factors such as poverty, racism, and income inequality, are interrelated. Because these factors have been linked to child maltreatment, providing timely mental health services for children and families involved in the child welfare system is especially important.

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)0.004
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 score0.405
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.348
Teacher spread0.296 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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