Canadians’ Attitudes Toward Community Reintegration of Those with a Mental Health Disorder Who Have Committed a Sexual Offense
Bibliographic record
Abstract
Compared to other criminal offenses, individuals convicted of a sexual offense experience higher levels of stigmatization, with the public often expressing feelings of fear and disgust. Many individuals who serve time for a sexual offense will reintegrate back into society amongst community members who often believe that sex crimes are on the rise, recidivism rates are high, and that individuals who commit sexual offenses are more dangerous than those who commit other offenses. Little research has explored how the presence of a mental health disorder impacts public attitudes toward reintegration. The current study uses thematic analysis with reflexive elements to explore attitudes toward individuals with a mental health disorder who have committed a sexual offense. Participants ( N = 262) were randomly assigned a vignette depicting an individual who has either schizophrenia or depression and is reintegrating into their neighborhood after committing a sexual offense. Findings suggest that while many participants support reintegration and rehabilitation, attitudes varied when considering the mental health disorder; some participants attributed offending behavior to schizophrenia, while others—particularly in the depression condition—suggested mental illness is not associated with sexual offending. Implications for mental health providers and future research directions are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".