Implementation research in forensic mental health: a scoping review
Bibliographic record
Abstract
BACKGROUND: Forensic mental health (FMH) serves as a critical juncture between the mental health and criminal justice systems. Factors on multiple levels - including sociopolitical, organizational, and individual- pose challenges to conducting implementation research in these settings. This hinders the uptake of evidence-based interventions and improvements to patient outcomes. This study examined implementation research conducted in FMH settings to understand its current state and inform future implementation research and practice. METHODS: We conducted a scoping review following the Joanna Briggs Institute methodology. A comprehensive literature search was performed across seven databases from their inception through April 2024, supplemented by searches in Google Scholar and six review studies, to identify relevant research. We analyzed included studies descriptively to explore determinants, strategies, and outcomes associated with the implementation of evidence-, or policy-based interventions in FMH. RESULTS: Of the 1327 records retrieved, 41 implementation studies were included. All studies were conducted in high-income countries and focused on interventions such as risk assessment, rehabilitation, patient support, and technology interventions, primarily using qualitative approaches. Key determinants for implementing interventions in FMH included individual characteristics (e.g., motivation, capacity) and inner setting factors (e.g., intervention compatibility with existing practices, access to knowledge and information). Various strategies, such as using evaluative and iterative strategies, training and educating stakeholders, changing infrastructure, and engaging consumers have been used to facilitate intervention uptake in FMH. Implementation outcomes primarily focused on uptake, fidelity, and acceptability. CONCLUSIONS: There is a clear need for more implementation research using rigorous study designs in FMH. Multilevel implementation strategies should be employed to address barriers from both the inner settings and individual characteristics, thereby promoting the successful implementation of interventions in FMH. Future implementation research should incorporate a health equity lens throughout the research process to enhance inclusivity and improve reporting on implementation strategies to support replications of interventions in FMH.
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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 | 0.112 | 0.316 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.040 | 0.042 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".