Spirituality and Moral Injury Among Military Personnel: A Mini-Review
Bibliographic record
Abstract
Introduction. Moral injury (MI) results when military personnel are exposed to morally injurious events that conflict with their values and beliefs. Given the complexity of MI and its physical, emotional, social, and spiritual impact, a holistic approach is needed. While the biopsychosocial aspects of MI are more commonly addressed, less is known of the spiritual dimension and how to incorporate it into treatment that facilitates restoration of one’s core self and mending of relationships with self, others and the sacred/Transcendent. The purpose of this study was to gain a greater understanding of the relationship between spirituality/religion (S/R) and MI as experienced by military members and veterans and to consider how S/R might be better integrated into prevention and treatment strategies. Methods. A mini review of peer-reviewed articles published between January 2000 and April 2018 regarding the relationship between spirituality and MI among military personnel and veterans was conducted. Results. Twenty-five articles were included in the final review. Five themes were identified and explored, including (i) Spirituality: A potential cause of and protective factor against MI, (ii) Self and identity: Lost and found, (iii) Meaning-making: What once was and now is, (iv) Spirituality as a facilitator of treatment for MI, and (v) Faith communities: Possible sources of fragmentation or healing. Discussion. Findings identified a cyclical relationship between S/R and MI, whereby S/R can both mitigate and exacerbate MI, as well as be affected by it. Seen as a type of S/R struggle, the use of S/R-specific strategies (e.g., forgiveness, review of S/R beliefs, engagement in S/R practices and (re)connection with S/R communities), integration of S/R perspectives into general interventions, and help from Chaplains may support healing, self-regulation, and mending of relationships, moral emotions and social connection. Further research is yet needed, however, regarding: (i) S/R orienting systems, interventions, practices and rituals/ceremonies that might protect against and treat MI, (ii) features of individuals who do/do not experience MI, (iii) S/R assessment tools and interventions, and (iv) ways to maximize the positive contributions of faith communities.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".