Well-being and mental health interventions for Indigenous people in prison: systematic review
Bibliographic record
Abstract
BACKGROUND: Indigenous people are overrepresented in prison populations of colonised justice systems, and Indigenous prisoners in these countries are at a particularly high risk of poor mental health and well-being. There is an acute need to ensure the access of these groups to culturally appropriate, evidence-based interventions. AIMS: To conduct a systematic review, evaluating quantitative and qualitative evaluations of mental health and well-being interventions designed for Indigenous people in custody. METHOD: A search of relevant peer-reviewed journal articles to August 2019 was conducted. The focus was on colonised countries under a Western model of justice and health, including Canada, Australia, New Zealand and the USA. The review utilised Scopus, Web of Science, PubMed, PsycNET, EBSCO, Proquest Criminal Justice Database and Informit. RESULTS: Of the 9283 articles initially found, only three quantitative and two qualitative evaluations of mental health or well-being interventions for Indigenous people in custody were identified. None were randomised controlled trials. Culturally based interventions appeared to have high acceptability and potential for increased recovery from trauma, reduced alcohol-related problems and lower reoffending. However, no studies quantitatively assessed mental health or well-being outcomes. CONCLUSIONS: As yet there is no high-quality evidence on the impact on mental health and well-being from interventions specifically for Indigenous prisoners, although existing studies suggest programme features that may maximise acceptability and impact. There is a moral, social and practical imperative to build a strong evidence base on this topic.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".