The Association between Mental Health Diagnoses and Trial Competency Assessments in Defendants: A meta-analysis
Bibliographic record
Abstract
In the realm of trial competency evaluations, there are a variety of methods used to evaluate whether an individual is fit to stand trial. Presently, forensic psychologists conduct trial competency evaluations in order to assess one’s ability to stand trial, but for persons with a mental health diagnosis, the generic competency measures are not the most effective means to assess one’s ability to stand trial, as mental health diagnoses impair cognitive functions that are required in judicial proceedings. Forensic psychologists have opted to utilize other assessment methods such as the MacArthur Competence Assessment Tool – Criminal Adjudication [MacCAT-CA] and Fitness Interview Test [FIT] to determine trial competency within this population. This meta-analysis aims to estimate the association between trial competency and mental health among criminal defendants. Within this analysis, two different moderators were studied to estimate if they had an impact on the level of association between trial competency and mental health which were the proportion of females and the total mental health score. Studies that measured the same relationships between trial competency and mental health for samples based in the United States and Canada were reviewed and coded to determine their overall effect.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.036 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".