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Record W4401350759 · doi:10.3390/youth4030073

Community-Based Alternatives to Secure Care for Seriously At-Risk Children and Young People: Learning from Scotland, The Netherlands, Canada and Hawaii

2024· article· en· W4401350759 on OpenAlexaboutno aff
Kate Crowe

Bibliographic record

VenueYouth · 2024
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsHarmLegislatureSeclusionPsychological interventionObligationPolitical sciencePublic administrationNursingMedicineLawPsychiatry

Abstract

fetched live from OpenAlex

This article identifies community-based alternatives to secure care being utilised in The Netherlands, Canada, Hawaii and Scotland. These countries offer ways to not only reduce or eliminate the need to deprive children and young people of their liberty in secure care but also reduce rates of child removal and alternative care placements. Secure care is the containment of children and young people, often subject to child protection interventions and residing in residential care, in a locked facility when they pose a significant risk of harm to the community and themselves. An admission to secure care exposes children to restrictive practices, such as seclusion, use of force and restraint. Jurisdictions have an ethical imperative, and often legislative obligation, to ensure there are less intrusive community-based supports available, which could be utilised instead of a secure care admission where possible. However, there is little research on what alternatives effectively divert secure care admissions. Hawaii, Canada, The Netherlands and Scotland demonstrate how countries can reduce the number of vulnerable children deprived of their liberty and exposed to restrictive practices by enhancing research linkages, responding to the voice of lived experience, and positioning secure care and alternatives within system-wide reform.

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 score0.217
Threshold uncertainty score0.447

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 studies0.0010.000
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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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