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Record W4414497980 · doi:10.26681/jote.2025.090205

Impact of an Educational Workshop on Occupational Therapy Student Perceptions of People with Justice System Involvement

2025· article· en· W4414497980 on OpenAlexaff
Emily Simpson, Sara Engimann, Jamie N Deal, Sydney M Sabbagha

Bibliographic record

VenueJournal of Occupational Therapy Education · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsDucks Unlimited Canada
Fundersnot available
KeywordsEconomic JusticeEmpathyCriminal justiceStigma (botany)PerceptionOccupational therapyQualitative researchLived experience

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.155
GPT teacher head0.571
Teacher spread0.416 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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