Determinants of readiness to implement forensic patient-oriented research: a study of barriers and facilitators in a high-secure hospital
Bibliographic record
Abstract
Introduction: Forensic mental health care is intended to promote recovery and reintegration, but is often experienced by patients as punitive and aversive. Forensic patients are rarely engaged in research to explore what matters most to them, and little guidance exists on how this engagement may be facilitated. In this paper, we explore perceived determinants of readiness to implement forensic patient-oriented research in a high-secure setting. Methods: Following a period of engagement with staff and patients in the high-secure setting, we conducted interviews with 30 staff members (including clinicians, researchers, and hospital leaders) and five patients. We analyzed interviews using a thematic analysis approach. Coding was initially informed by the Consolidated Framework for Implementation Research, and subsequent iterations of analysis extended beyond this framework to explore patterns of meaning encompassing multiple implementation domains. Results: We identified three themes in our data: "Navigating a climate of distrust, discrimination, and restricted autonomy"; "Hearing and interpreting patient voices"; and "Experiencing a slow shift in the tide." The first two themes represent potential challenges, including distrust and stigma; inherent restrictions in forensic care, and perceptions that patient autonomy threatens staff safety; patient fears of repercussions; and barriers to valuing and understanding patient voices. The third theme describes the ongoing shift towards patient-centredness in this setting, and participants' interest in proceeding with forensic patient-oriented research. Discussion: Increased attention to relationship-building, trauma-informed principles, and epistemic injustice (i.e., unfair devaluing of knowledge) in high-secure settings can support the involvement of forensic patients in research.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".