Assessing Fitness to Stand Trial: The Utility of the Fitness Interview Test (Revised Edition)
Bibliographic record
Abstract
OBJECTIVE: In Canada most evaluations of fitness to stand trial are conducted on an inpatient basis. This costs time and money, and deprives those defendants remanded for evaluation of liberty. This research assessed the predictive efficiency of the Fitness Interview Test, revised edition (FIT) as a screening instrument for fitness to stand trial. METHOD: We compared decisions about fitness to stand trial, based on the FIT, with the results of institution-based evaluations for 2 samples of men remanded for inpatient fitness assessments. RESULTS: The FIT demonstrates excellent utility as a screening instrument. The FIT shows good sensitivity and negative predictive power, which suggests that it can reliably screen those individuals who are clearly fit to stand trial, before they are remanded to an inpatient facility for a fitness assessment. CONCLUSION: We discuss the implications for evaluating fitness to stand trial, particularly in terms of the need for community-based alternatives to traditional forensic assessments.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".