Occupation Displacement and Community Corrections Offenders: The Usefulness of Concepts of Occupation on Assessment, Intervention, and Policy in Community Corrections
Bibliographic record
Abstract
Purpose: Criminal justice settings significantly restrict an inmate’s occupational opportunities and promote occupational injustice and deprivation (Muñoz & Farnworth, 2009). Including healthy occupational choices and engagement opportunities in treatment, and measures of engagement in outcomes provides a more hopeful and potentially effective way to measure success in community reintegration of offenders (Molineux & Whiteford, 1999). This mixed-method study examined the utility of occupation-based assessments and examined the connections between occupational engagement or, role diversity, and substance abuse.\nMethods: Participants: Forty volunteers (male N=32) residing in a community corrections facility, mean age of 34.33. Ninety percent had been incarcerated for less than 2 years and 87% crimes were related to substance abuse. Measures: Canadian Occupational Performance Measure (COPM); Occupational Self-Assessment (OSA); Semi-structured interviews; Demographic data (Clarke, 2003; Eggers, Munoz, Sciulli, & Crist, 2006). Analysis: Interview data examined in relation to COPM/OSA data and for examples and descriptions of occupational displacement, impoverishment, and alienation.\nResults: COPM results are consistent with the criminal justice literature indicating that offenders rate performance skills higher, but with lower satisfaction with their performance. OSA average scores indicated that offenders typically have higher rated performance (28.41), than habituation (12.03) and volition (11.87), but have few healthy habits and volition to support successful skill application. The greatest occupational limitations on OSA items were: making decisions based on what they think is important; getting done what they need to do; having a satisfying routine; and working toward goals. The items considered most important were being involved as a student, worker, volunteer, family member; and working toward goals. Most participants asserted that alcohol or drug use had stopped them from achieving goals and making changes in their lives. This suggests a rationale for occupation-based intervention to give meaning, purpose and structure to clients' abstinence-related goals.\nOngoing qualitative data analysis identified examples of occupational displacement (White, 1999), occupation alienation, and impoverishment of occupational options, supporting the idea that chronic substance abuse displaces time and interest available to engage in other occupations. Even when in recovery, occupational impoverishment persists due to strength of habits established during chronic abuse contributing to alienation in occupations that are pursued.\nSummary: We expand the concept of occupational displacement and its contribution to occupational science, and build a case for inclusion of occupation-based assessments intervention to support successful community reintegration and counter occupational injustices.\nObjectives for Discussion Period: Discuss application of occupational justice theories in criminal justice settings as opposed to more traditional criminal justice rehabilitation approaches. Discuss the potential to use the data to promote a more systematic and consistent measures of outcomes in criminal justice settings that may promote increased occupational justice in policies and programs. Discuss the relevance and application of the concept of occupational displacement in this study and its potential usefulness in occupational science research. \nOr framed as discussion questions: How might understandings of occupational justice be used to influence policy in criminal justice? Which occupational science concepts are likely to be the most powerful in crafting policies that promote occupational justice toward rehabilitative outcomes in correctional settings? What quantitative and qualitative tools would likely yield the most fruitful outcomes for informing criminal justice policies and programs to minimize occupational injustices in corrections. Considering a definition of Occupational displacement as: “when the demands of engaging in one occupation rule out, or at least place obstacles in the way of, engaging in another occupation, formerly pursued on a regular basis.” (White, 1999, p. 163), how useful is this concept in researching occupation, especially in populations in which the displacement is related to chronic substance abuse? What other populations experience occupational displacement for which occupational science research would be beneficial and what would such a study look like?
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".