Relationships between patient-level factors and criteria for fitness to stand trial
Bibliographic record
Abstract
In Western criminal justice systems, proceedings may be halted if an individual is deemed mentally unfit to stand trial. As a prerequisite to adjudication fitness to stand trial can be evaluated through structured or unstructured assessments. Previousstudies suggest limited use of structured assessments in clinical practice. Few studies have looked at the success of unstructured measures of psycholegal abilities, and fewer still have investigated the influence of individual variables on criteria for fitness to stand trial. The purpose of the present study was to examine the relationship between variables relevant to opining fitness as determined by previous research and the criteria for fitness to stand trial. The study yielded significant correlations between the three criteria for fitness to stand trial and the following variables: impaired mental status during assessment, presence of intellectual disability, nature of index offence, socioeconomic status, and all unstructured measures of psycholegal abilities. These results suggest that unstructured clinician assessment of fitness to stand trial can be successful at determining fitness and fulfillment of the three underlying criteria, and further clarify the role of specific symptoms on opinions of unfitness. Future directions for research in the areas of structured professional judgment and fitness restoration are discussed.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".