Culturally specific interventions and Indigenous practices in correctional and forensic settings: a systematic review
Bibliographic record
Abstract
While professional guidelines, legislation and research emphasize the importance of adapting treatment approaches to patient characteristics and culture (American Psychological Association [APA], 2021, 2019; Correctional Service of Canada [CSC], 2013; Canadian Psychological Association [CPA], 2017; Canadian Counseling and Psychotherapy Association [CCPA], 2021), psychological and forensic research and practice related to culturally specific programming lags behind expectations to meet the needs of ethnically diverse clients. Given the continued overrepresentation of minorities within forensic institutions (Aguiar, 2020; Carter & Forsyth, 2007; Hanser & Gomila, 2015; Leguizamo et al., 2018), this systematic review aimed to raise awareness of culturally-specific interventions and Indigenous practices in correctional and forensic settings by being the first to examine their use and synthesize data on their recidivism, mental health, and behavioral outcomes with incarcerated individuals and forensic patients in the United States (U.S.) and Canada. Following PRISMA guidelines, the authors identified 8 articles from an initial total of 6,986 that met selection standards. All studies included Indigenous or Aboriginal populations and 7 of 8 studies were conducted in Canada. Results from deductive content analysis revealed that 62.5% of the studies – all correctional – examined culturally specific programs (i.e., Pathways Program, Native Sisterhood, Work 2 Give, Prison Garden, Aboriginal Programming). The majority of studies (87.5%) in both correctional and forensic settings examined Indigenous practices (use of Elders, sweat lodge ceremonies, spirituality). Although only 37.5% of the studies contained recidivism data (showing mixed results), 87.5% reported mostly positive mental health and behavioral outcomes data. This study appears to be the first to bring together research on the value of Elders and traditional ceremonies (i.e., sweat lodge, pipe ceremonies, sacred circles) in the rehabilitative and healing journeys of correctional inmates and forensic psychiatric patients. These findings highlight the need to widen the scope of culturally-specific programing for minority groups overrepresented in correctional and forensic contexts and improve consistency in terminology and methodological rigor when studying culturally-specific interventions and their outcomes in these settings.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".