Role of morality in the experience of guilt and shame within the armed forces
Bibliographic record
Abstract
OBJECTIVE: Despite advances in our understanding of mental health issues among military forces, a large proportion of military personnel continue to exhibit deployment-related psychological issues. Recent work has identified symptoms of guilt and shame related to moral injury as contributing significantly to combat-related mental health issues. This systematic scoping review explores the association between morality and symptoms of guilt and shame within military forces. METHOD: A search of the literature pertaining to guilt, shame and morality within military samples was conducted. RESULTS: Nineteen articles were selected for review. There is strong evidence linking exposure to and the perceived perpetration of moral transgressions with experiences of guilt and shame. Critically, symptoms of guilt and shame were related to adverse mental health outcomes, particularly the onset of post-traumatic stress disorder (PTSD). No studies have explored moral judgment in conjunction with assessments of guilt or moral injury. CONCLUSION: These findings have important implications for the prevention and treatment of PTSD-related symptoms in military samples. By measuring moral judgment prior to deployment, it may be possible to predict the likelihood of incurring moral injuries and the development of associated symptoms. Early intervention programmes aimed at ameliorating guilt and shame are required to prevent the long-term development of deployment-related psychological distress.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".