An evaluation of fitness to stand trial assessment practices across Canada
Bibliographic record
Abstract
Forensic assessments play a crucial role in the Canadian criminal legal system. One of the most common forensic assessments is fitness to stand trial evaluations, which determine whether an individual can competently engage with the legal system. While the United States is currently facing a “competency crisis” due to overwhelming demand for fitness assessments, the extent to which Canada is experiencing similar concerns is unknown. The present study used a mixed methods exploratory design to survey Canadian forensic mental health (FMH) service providers to a) capture a snapshot of the FMH services available in each province and b) determine and compare each province’s current demand and capacity to meet demands for fitness evaluations. Forty FMH sites and 2031 designated inpatient forensic beds were identified across Canada, representing a 16% increase in sites and 31% increase beds since 2006. Thus far, data has been captured from 12 of these sites. The study also conducted semi-structured interviews of service providers involved in the operation of FMH sites across Canada to identify factors influencing our ability to meet fitness demands and highlight recommendations for policy and practice. Reflexive thematic analysis of study interviews (n = 11) identified four themes in participant responses including Challenges to Providing FMH Care, A Growing Burden on the FMH System, Stigma and Lack of Support, and Identified Needs and Attempts at Change. This study underscores the urgent need for enhanced communication, education, and standardized data collection across Canadian FMH services, alongside expanded forensic training and broadening the scope of practice for forensic psychologists. Addressing these issues is essential for averting further crisis in Canada and ensuring just and efficient FMH care.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".