Forgiveness: A Key Component of Healing From Moral Injury?
Bibliographic record
Abstract
Service members and veterans can be exposed to potentially traumatic and morally injurious experiences (PMIEs) including participating in, witnessing, or failing to prevent an act(s) that transgresses their core beliefs. Violation of one's deeply held morals and values can be profoundly distressing and shatter one's sense of self at the deepest level. Relationships with self, others, the world, and for some, the Sacred, can also be fractured. Post-Traumatic Stress Disorder (PTSD) and/or Moral Injury (MI) can result. Left unresolved, MI can leave individuals struggling with guilt, shame, cognitive dissonance, and negative self-attributions. A holistic approach that addresses the psychological and spiritual harm associated with MI is warranted. We wonder if forgiveness can help individuals struggling with MI to address the harm caused by actions or inactions, release negative emotions, and mend relationships. Commonly used by Spiritual/Religious (S/R) Leaders, forgiveness practices are increasingly being explored by Mental Health Professionals as a complement to evidence-based treatment approaches. This article provides case examples that illustrate the use of forgiveness practices that promote recovery and identifies programs used in clinical practice that incorporate forgiveness. Research is yet needed to better understand the importance of forgiveness in the treatment and healing of PTSD and/or MI. This requires an interdisciplinary discourse between Mental Health Professionals and S/R Leaders working in the field of MI. Such engagement and integrated use of forgiveness practices may yield improved outcomes not only for service members and veterans, but for all those struggling as a result of PTSD and/or MI.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".