Spiritual Dimensions of Moral Injury: Contributions of Mental Health Chaplains in the Canadian Armed Forces
Bibliographic record
Abstract
Moral Injury (MI) describes the profound distress experienced by military personnel as a result of a violation of personal beliefs. Impacting not only psychological, but spiritual, health, and well-being, MI is associated with spiritual/religious (S/R) suffering and a need to find hope, trust, connection, reconciliation, and wholeness. Addressing spiritual wounds can help military personnel overcome fundamental barriers that may impede them from effectively engaging in or benefitting from traditional trauma therapies and having a more complete recovery. Military Chaplains in the Canadian Armed Forces (CAF) are both embedded with the troops in garrison and theater and work closely with service providers such as the Royal Canadian Medical Services. In their role, they offer front-line support and services to members and their families and facilitate access to care. Specific to the assessment and treatment of MI, Mental Health Chaplains (MHCs) offer S/R expertise and a complimentary clinical skill set to service members and interdisciplinary teams. This perspectives article explores the S/R dimension of MI, discusses the role of MHCs in CAF Mental Health (MH) Clinics, and provides clinical perspectives of a MHC regarding the treatment of MI. Key focuses of MHC interventions include bridging to other mental health services and supports, facilitating S/R coping and grounding, reconciling worldviews, resolving anger at a God-figure (not specific to any S/R perspective) and fostering reconciliation. Based on the literature, Mental Health practitioner's feedback, and clinical experience, MHCs are integral to service provision regarding MI and warrant more widespread inclusion on interdisciplinary teams in CAF MH Clinics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".