The influence of a Learning to Forgive Program on institutional offending and recidivism among offenders with mental disorder
Bibliographic record
Abstract
Previous researchers have demonstrated that learning to forgive may reduce the likelihood of offending/reoffending. Forgiveness therapy may be useful for rehabilitation by assisting traumatized individuals to release revengeful emotions. The current study is a follow up to a previous study that examined the effects of a 6-week forgiveness psychoeducational intervention for offenders with mental disorders. The aim of the current study was to determine any differences for participants who received a forgiveness intervention versus a control group for rates of recidivism (likelihood of reoffending and length of time to reoffend) and type of institutional offense. Recidivism data was collected through the Canadian Police Information Center. Both the control and treatment group in this study were selected from offenders with mental disorder at the Regional Psychiatric Centre, a multilevel forensic psychiatry hospital in Saskatoon, Canada. Results indicated that participants who received the forgiveness intervention took significantly longer than the control group to both commit non-violent offenses, and to be convicted of any offense. Results suggest that forgiveness therapy for offender populations may improve behavior and reduce recidivism.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".