Immigrant, racialised and ethno-culturally diverse communities and community treatment orders: A scoping review
Bibliographic record
Abstract
BACKGROUND: People with serious mental illness from immigrant, racialised and ethno-culturally diverse communities experience greater coercion in mental health care. AIMS: This review aims to scope the literature and synthesise findings relevant on the association between these groups, and the use of Community Treatment Orders (CTOs) and related forms of compulsory community treatment. METHOD: Five electronic databases were searched to identify relevant studies. Studies were included if they directly examined the association between immigrant, racialised and ethno-culturally diverse communities and community treatment orders and related forms of treatment or presented relevant data as part of a larger study. RESULTS: Of the 43 studies included 9 studies focussed directly on associations between immigrant, racialised and ethno-culturally diverse communities and CTOs, while 34 studies presented relevant data related to immigrant, racialised and ethno-culturally diverse communities and CTOs albeit not the primary focus of the study. Most studies were quantitative, with varied study designs. The majority of studies were from Australia and New Zealand. followed by North America and the UK. Of the nine studies focussing directly on ethnicity and CTOs, results were mixed, and varied based on design, population and jurisdiction. CONCLUSIONS: The relationship between ethnicity, immigration status and CTOs is complex, with mixed findings across jurisdictions. Most of the literature comes from Australia and New Zealand, were Indigenous populations were a significant focus. The review highlights the need for more qualitative and quantitative research, especially in underrepresented ethnic groups and jurisdictions outside of Australia and New Zealand.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".