Community-Based Alternatives to Secure Care for Seriously At-Risk Children and Young People: Learning from Scotland, The Netherlands, Canada and Hawaii
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".