MORAL INJURY IN THE FORENSIC PSYCHIATRIC POPULATION
Bibliographic record
Abstract
This research investigates the intricate interplay between the moral emotions of shame and guilt, within justice-involved populations, with a special focus on those deemed Not Criminally Responsible (NCR) due to Mental Disorder. Recognizing the pivotal role of offense-related shame and guilt in motivating behavior and influencing psychological functioning, we conducted an extensive investigation to underscore the significance of acknowledging moral injury (MI) and its symptoms within this context. By synthesizing two comprehensive studies, our objective was twofold: to shed light on the prevalence and effects of shame and guilt, and to introduce the concept of moral injury as a fundamental lens for understanding their impact. In the first study, we examined the influence of shame and guilt on motivating behavior and psychological well-being among offending populations. We found that shame consistently relates to adverse outcomes, including defensive behaviors, self-loathing, and externalizing behaviors such as blame-shifting. Contrary to our predictions, guilt was associated with constructive responses, such as self-forgiveness, empathic concern, and assuming responsibility for one's actions. However, both shame and guilt contributed to the risk of recidivism among certain offenders. This study illuminates the intricate dynamics between moral emotions, psychopathology, and recidivism, underscoring the need to acknowledge the differential influences of the moral emotions, shame, and guilt. In the second study, we developed and validated the Moral Injury Screener in the Offending Population NCR (MIO-NCR), a self-report measure that assesses MI in justice-involved individuals, particularly NCR individuals. Through rigorous psychometric analysis, the MIO-NCR demonstrated promising criterion and construct validity. Our findings emphasized the centrality of guilt and betrayal in MI experienced by NCR individuals, aligning with contemporary syndromal definitions. The MIO-NCR, an invaluable tool, enables promising identification of MI within the NCR population. By consolidating these studies, we found that shame and guilt manifest profoundly withinthe justice-involved population, underscoring the value of MI and its core symptoms. The current thesis not only reaffirms the importance of understanding moral emotions but also advances knowledge on MI within this unique context. Our research provides a framework for developing a comprehensive approach to intervention and rehabilitation that recognizes the intricate relations between moral emotions, psychopathology, and recidivism, ultimately fostering healthier outcomes for justice-involved individuals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".