Impact of an Educational Workshop on Occupational Therapy Student Perceptions of People with Justice System Involvement
Bibliographic record
Abstract
The United States has the highest number of people per capita in detention centers when compared to all other nations in the world. Stigma exists against people with justice system involvement, resulting in discrimination by healthcare providers and students and ultimately health inequities. This convergent mixed-methods study aimed to impact occupational therapy students’ (N=25) knowledge, attitudes, and beliefs about the criminal justice system and people with justice system involvement. The workshop included education, a panel of people with lived experience, and an occupational therapist with experiences in the criminal justice system. Significant changes were seen in the number of correct responses to 10 out of 14 knowledge questions and changes in 13 out of 20 statements related to beliefs and attitudes. Qualitative themes included: shifts in perspectives influenced by stigma, power of lived experience, and need for responsive curriculum. Findings indicate that students are impacted by stigma about people with justice system involvement but that they are receptive to learning and reconsidering beliefs, especially when exposed to people with lived experience. This has the potential to apply to other historically marginalized populations, which may enhance student empathy and encourage interest in emerging practice areas.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".