Patient engagement in forensic mental health care: a scoping review
Bibliographic record
Abstract
QUESTION: This scoping review aimed to examine the state of research on patient engagement in forensic mental health (FMH) care to inform future research, practice and policy development. STUDY SELECTION AND ANALYSIS: A systematic literature search was conducted in Medline, Embase, CINHAL, PsycINFO and EBSCO from database inception to June 2024, supplemented by grey literature and reviews. We analysed the included studies descriptively and narratively. FINDINGS: Of the 7010 records retrieved, 73 studies were included. Research on patient engagement in FMH has increased since 1999, with all studies conducted in high-income countries and the majority (64%) employing qualitative designs. The focus was primarily on risk assessment and management, recovery and therapeutic or medication interventions. Most patient participants were male, white and diagnosed with schizophrenia, personality disorders or substance use disorders. Nurses were the major staff participants. The levels of engagement were typically involvement and collaboration. Commonly reported outcomes were a sense of engagement and risks of violence and aggression. We identified barriers and potential strategies for patient engagement across five levels: patient, staff, process, organisational and sociopolitical. Barriers to patient engagement included, but were not limited to, patients' mental health conditions, paternalistic staff attitudes and power imbalances. Potential strategies to enhance patient engagement were identified, such as the adoption of recovery-oriented care models. CONCLUSIONS: Patient engagement in FMH is hindered by multilevel barriers, requiring coordinated efforts from policymakers, organisational leaders, professionals and patients to facilitate its integration into routine practice. Greater attention is needed to ensure the meaningful engagement of marginalised populations and patients from low and middle-income countries.
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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 | 0.034 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| 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".