Moral Injury in Military Operations: A Review of the Literature and Key Considerations for the Canadian Armed Forces
Bibliographic record
Abstract
Abstract : As the Canadian Armed Forces (CAF) regroup from its largest deployment since Korea and the longest combat deployment since the Second World War, emerging mental health data suggests that approximately 14 of CAF personnel who had deployed to Afghanistan had a mental health disorder that was linked to the Afghan mission. This paper focuses on a particular psychological aftermath of military operations, that which may be associated with the moral and ethical challenges that personnel face in military missions. More specifically, in this paper I provide an introduction to the concept of moral injury, formally defined as the psychological anguish that can result from [p]erpetrating, failing to prevent, bearing witness to, or learning about acts that transgress deeply held moral beliefs and expectations (Litz et al., 2014, p. 697). I begin with a brief overview of the essential role of morality and ethics in military operations. I then outline the historical development of the concept of moral injury, discuss its symptomology, and outline the current approaches to treatment. I conclude by discussing anumber of key considerations for the CAF in terms of a way ahead with respect to the issue of moral injury.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".