Mental health treatment programs for children and young people in secure settings: A systematic review
Bibliographic record
Abstract
BACKGROUND: While there are mental health treatment programs for children and young people in secure settings (i.e., secure treatment programs) in many countries, there is a lack of transparency and consistency across these that causes confusion for stakeholders and challenges for the design and delivery of high-quality, evidence-based programs. This systematic review addresses two questions: What do mental health treatment programs for children and young people in secure community settings look like across jurisdictions? What is the evidence underlying the various components of these programs? METHODS: Twelve databases were searched in November 2021: CINAHL, EMBASE, MEDLINE, PsycINFO, PubMed, Scopus, Science Direct, Academic Search Complete, Psychology and Behavioral Sciences Collection, Google Scholar, OpenDOAR, and GreyLit.org. To be included, publications had to be empirical literature or a report on mental health treatment within a secure setting for people under the age of 25; contain pre-identified keywords; be based on a research or evaluation study conducted since 2000; and be assessed as low risk of bias using an adaptation of the Critical Appraisal Skills Programme qualitative research checklist. The systematic review included 63 publications. Data were collected and analyzed in NVivo qualitative software using a coding framework. RESULTS: There are secure treatment programs in Australia, Belgium, Canada, New Zealand, the Netherlands, England and Wales, Scotland, and the United States. Although there are inconsistencies across programs in terms of the systems in which they are embedded, client profiles, treatments provided, and lengths of stays, most share commonalities in their governance, definitions, designs, and intended outcomes. CONCLUSIONS: The commonalities across secure treatment programs appear to stem from them being designed around a need for treatment that includes a mental disorder, symptom severity and salience involving significant risk of harm to self and/or others, and a proportionality of the risks and benefits of treatment. Most share a common logic; however, the evidence suggested that this logic may not to lead to sustained outcomes. Policymakers, service providers, and researchers could use the offered recommendations to ensure the provision of high-quality secure treatment programming to children and young people with serious and complex mental health needs.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".