Using Multiple Exemplar Training to Increase Fitness to Stand Trial
Bibliographic record
Abstract
Fitness to stand trial refers to an individual’s capacity to comprehend judicial proceedings related to a crime they have committed. There currently exists no legally codified or empirically validated procedures for training fitness in forensic inpatients with mental health diagnoses, developmental diagnoses, or dual diagnoses. The purpose of this study was to develop a teaching procedure based on multiple exemplar training (MET) that provides a procedural foundation for training fitness. Broadly, this study sought to yield the first objective teaching procedure and measurement system for improving fitness based on the principles of behaviour analysis. Specifically, MET was used to develop a variety of stimulus (questions) and response topographies, which were presented to participants and designed to increase acquisition of targets related to fitness. Results from one completed participant and three partial datasets provide preliminary to support MET as a procedure for increasing fitness, as indicated by an increase in correct responding across to all questions related to an individual’s fitness to stand trial. This study has implications for both judicial system and hospital settings, as the empirical validation of a standardized approach to training fitness could serve to streamline service delivery and mitigate the common barriers experienced by individual with dual diagnoses during legal proceedings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".