Conducting Patient Oriented Research (POR) in a Forensic Psychiatric Facility: A Case Study of Patient Involvement
Bibliographic record
Abstract
Patient oriented research (POR) is new to the healthcare research landscape in Canada and has not yet been applied to the forensic field. This review begins by introducing POR, the POR Level of Engagement Tool, and complimentary approaches used in research with forensic patients. Next, the potential key challenges, paradoxes, and benefits of applying POR to a forensic mental health setting are presented. Drawing on this understanding, a review of our team’s experiences applying the POR Level of Engagement Tool at the Regional Psychiatric Center, a Canadian forensic psychiatric facility, is presented as a case study. The research question supported by our patient advisors and addressed with patients and facility staff was: “What topics do you think we need to know more about to benefit patients at the Regional Psychiatric Center?” We conclude this review article with recommendations on how to meaningfully, and practically, involve forensic psychiatric patients diagnosed with mental disorders in POR when initiating a project. Forensic psychiatric patients can provide insightful knowledge based on their experiences of mental illness to improve the prison health care system and practices.
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".