The influence of a learning to forgive programme on negative affect among mentally disordered offenders
Bibliographic record
Abstract
BACKGROUND: Clinical trials and meta-analyses provide some evidence for effectiveness of forgiveness therapy delivered individually or in groups. To date, however, forgiveness therapy has not been evaluated with mentally disordered offenders. Given the high prevalence of experienced and perpetrated trauma among such people, this population may particularly benefit from such an intervention. AIM: The aim of this study is to test the feasibility and impact of a 'learn to forgive' group programme among mentally disordered offenders on a specialist secure hospital setting. METHODS: We conducted a non-randomised trial with 36 offenders with mental disorders and 29 comparison patients. The intervention group engaged in a six-week manual-based 'learn to forgive' treatment programme, while the comparison group watched a 90-minute video on forgiveness. Both groups completed measures of anger, depression, stress, forgiveness and satisfaction with life at baseline and then 6 and 18 weeks later. A repeated measures mixed-effects model was used to investigate the association between affective outcomes and type of intervention received, after adjusting for baseline characteristics. RESULTS: The group completion rate was over 90%. The treatment and comparison groups were similar on baseline demographic and criminological measures, but the treatment group had higher baseline anger and depression scores. While both groups showed improved capacity to forgive and reduced negative affect over time, those in the 'learn to forgive' programme showed significantly more improvement in forgiveness and on anger measures. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Forgiveness training can be delivered effectively to offenders with mental disorders in clinical settings. Its range of benefits, including reduction I in anger as well as improved capacity to forgive, suggest that it may have longer term implications for personal safety and reintegration into mainstream societal settings. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".