Moral injury in women military members and Veterans: What do we really know?
Bibliographic record
Abstract
The term moral injury has been used to describe suffering military service members' experience of non-life-threatening traumatic events that violate their moral code, such as killing or injuring a non-combatant, witnessing a fellow service member harm a non-combatant, or betrayal by a trusted leader. Service members who experience morally injurious traumas may feel intense shame, guilt, anger, and a lack of forgiveness toward others or themselves. In extreme cases, they may feel unworthy of living. This article examines existing information and knowledge gaps about the morally harmful experiences of women service members and Veterans. Anecdotal findings have shown that women service members face potentially morally injurious events through combat as well as military sexual trauma. Most research on moral injury has been conducted with the military population. However, in the United States, much of the scholarship has primarily focused on the experiences of men service members and Veterans. Although women service members make up 20% of the military population, research is limited to military women's unique morally injurious experiences. Further study is needed to explore and understand what events women service members and Veterans identify as morally injurious and how they experience moral injury. Capturing these perspectives is imperative to identifying and treating moral injuries.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".