The role of occupational therapy in the criminal justice system : enhancing transitional services for community integration
Bibliographic record
Abstract
Justice-involved individuals often face barriers to employment, emotional regulation, and communication, which can hinder successful reentry and increase recidivism. These challenges reflect occupational injustice and highlight the need for supportive transitional services. Occupational therapy (OT) offers a holistic, client-centered approach to address these issues, yet its role in the criminal justice system remains limited. This doctoral capstone project developed and implemented a virtual, self-paced job readiness workshop in collaboration with Beyond the Conviction: Building Brighter Futures (BTCBBF), a nonprofit organization supporting individuals post-incarceration. Guided by the Canadian Model of Occupational Performance (CMOP), Model of Human Occupation (MOHO), and Occupational Adaptation (OA), the project followed a quality improvement approach using the Plan-Do-Study-Act (PDSA) cycle. A review of current literature supported the integration of OT in reentry programs. Pre- and post-surveys completed by BTCBBF staff and volunteers showed increased confidence and knowledge in communication, goal setting, and emotional regulation. Qualitative feedback emphasized the accessibility and real-world applicability of the workshop content. A sustainability guide was created to support long-term use. This project reinforces OT's potential to reduce recidivism and foster occupational justice by promoting skill development, independence, and positive social change for individuals reentering the community.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".