Gender-based Analysis of Criminogenic Risk and Clinical Need among Ontario Forensic Patients
Bibliographic record
Abstract
Persons suffering from mental illness in the criminal justice system are a heterogeneous group that require specialized services to meet their diverse needs (Mental Health Commission of Canada, 2012; Dupuis, MacKay & Nicol, 2013; Tusca et al., 2011; Penney et al., 2013; Jansman-Hart et al., 2011; MacPhail & Verdun-Jones, 2013; Seto, Harris, Rice, 2004; Chaimowitz, 2012; Tusca et al., 2012; Nowatzi & Grant, 2011). Further, while public safety is a high priority, basing decisions solely on security and reduction of risk do not support the recovery or rehabilitation of the clinical, social and functional needs of the forensic mental health population (Tusca et al., 2012). \nGender can have separate and interacting effects on mental health and criminogenic needs as men and women have different experiences in how they express symptoms associated with mental illness, in service utilization and sometimes in even how these symptoms are managed (Archambault et al., 2014; Eaton et al., 2012; Nowatzki & Grant, 2011; Ramsay et al., 2001; World Health Organization, 2008). Omission of gender in forensic mental health research limits the validity and overall generalizability of findings, which in turn can affect the treatment and services provided at the individual level (Nowatzki & Grant, 2011; Van Voorhis et al., 2010). \n\tUnderstanding gender differences in forensic mental health will allow for more refined patient-centered care. Patient-centered care addresses and integrates care for an individual’s multiple risk factors and conditions and is sensitive to their social context (Nowatzki & Grant, 2011). Further, among forensic inpatients it is crucial to incorporate criminogenic factors that reduce risk of recidivism (for example, substance abuse, harm to others); and clinical factors (for example, psychosis, anxiety); social factors (for example, social supports); functional factors (for example, cognitive impairment, IADL) that support recovery and rehabilitation of persons in the forensic mental health system (Chambers et al., 2009; Shrinkfield & Ogloff, 2014; Tusca et al., 2012). \nAim: This research focuses on a gender-based analysis of assessing the influences at both the individual and facility level that can predict three outcomes among forensic mental health patients in Ontario: \n1.\tRestriction to room (seclusion/confinement to room) in forensic mental health hospitals; \n \n2.\tUnaccompanied leaves from forensic mental health hospitals and; \n3.\tFreedom of movement (FoM) among forensic inpatients. \n \nResults: This thesis demonstrates that factors influencing the freedom of movement (FoM) among forensic mental health patients include not only indicators of violence, aggressive behaviour and risk of harm to others, but in fact include many clinical, social and functional characteristics. For example, substance use problems, lack of insight into mental health problems, functional impairment, higher scores on the RIIDE scale and being an adult at age of first police intervention for non-violent crime were found to decrease the odds of being in a higher level of freedom of movement (easing of restrictions) among forensic inpatients. Although public safety is one the factors to consider when easing a person’s restrictions, it is not the only factor that should be considered by forensic mental health teams. \nThere were notable gender differences found in the easing of restrictions among forensic mental health patients. For example, female inpatients with more aggressive behaviour were more likely to be confined to the unit whereas male inpatients demonstrating the same level of aggression were more likely to be restricted to room. Essentially, tighter restrictions are being placed on male forensic inpatients when similar aggressive behaviours are being exhibited compared with female forensic inpatients. \nConclusion: This thesis demonstrates that factors influencing the freedom of movement (FoM) among forensic mental health patients include not only indicators of violence, aggressive behaviour and risk of harm to others, but in fact include many clinical, social and functional characteristics. Although public safety is one the factors to consider when easing a person’s restrictions, it is not the only factor considered by forensic mental health teams. As well, this research demonstrated that it is important to consider both the individual and facility level characteristics when determining gender differences in factors associated with freedom of movement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".