The role of patient perspectives in forensic mental health: a study of progress in recovery and protective factors of risk for violence
Bibliographic record
Abstract
The assessment of violence risk and progress in recovery are prominent concerns in forensic psychiatry, with protective factors being recently incorporated in understanding the risk and recovery paradigm. However, there are still very few assessment tools that incorporate the patient perspective in forensic psychiatry. The following thesis explored patient self-assessments of protection and progress in recovery and assessed the degree of concordance with clinician and research-rated estimates of these constructs in a sample of 37 patients deemed Not Criminally Responsible for their crimes on account of mental disorder (NCRMD). Patient file reviews, patient-rated scales, clinician-rated scales and patient interviews were used to rate protective factors and risk factors for violence risk, and progress in recovery. Linear regression models revealed that work and education experience, criminal history and psychiatric history were not predictive of patient-clinician and patient-researcher concordance of protective factors for violence risk (SAPROF) and progress in recovery (DUNDRUM-3 and DUNDRUM-4). Criminal history alone was predictive of risk scores (HCR-20) and protection scores (SAPROF). Binary logistic regressions indicated that the aforementioned concordance was not significant in predicting whether a patient was assigned to a medium secure or general secure unit. A hierarchical binary logistic regression showed that protection scores did not provide additive validity to risk scores in predicting the level of security of patients. Implications and limitations are discussed. This study increases the understanding of protective factors for violence risk and progress in recovery, with an emphasis on patient perceptions and their concordance with the clinicians’ and researchers’ perceptions.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".