Managing Risk in a Culture of Rights: Providing Support and Treatment in Community-Based Settings for Persons with Intellectual Disabilities who Sexually Offend
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People with intellectual disability who sexually offend commonly live in community-based \nsettings since the closing of all institutions across the province of Ontario. Nine (n=9) front line staff who provide support to these individuals in three different settings (treatment setting, transitional setting, residential setting) were interviewed. Participants responded to 47 questions to explore how sex offenders with intellectual disability can be supported in the community to prevent re-offenses. Questions encompassed variables that included staff attitudes, various factors impacting support, structural components of the setting, quality of life and the good life, staff training, staff perspectives on treatment, and understanding of risk management. Three overlapping models that have been supported in the literature were used collectively for the basis of this research: The Good Lives Model (Ward & Gannon, 2006; Ward et al., 2007), the quality of life model (Felce & Perry, 1995), and variables associated with risk management. Results of this research showed how this population is being supported in the community with an emphasis on the following elements: positive and objective staff attitude, teamwork, clear rules and protocols, ongoing supervision, consistency, highly trained staff, and environments that promote quality of life. New concepts arose which suggested that all settings display an unequal balance of upholding human rights and managing risks when supporting this high-risk population. This highlights the need for comprehensive assessments in order to match the offender to the proper setting and supports, using an integration of a Risk, Need, Responsivity model and the Good Lives model for offender rehabilitation and to reduce the likelihood of re-offenses.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it