Moral Injury and Recovery in Uniformed Professionals: Lessons From Conversations Among International Students and Experts
Bibliographic record
Abstract
Introduction: In the course of service, military members, leaders, and uniformed professionals are at risk of exposure to potentially morally injurious events (PMIEs). Serious mental health consequences including Moral Injury (MI) and Post-traumatic stress disorder (PTSD) can result. Guilt, shame, spiritual/existential conflict, and loss of trust are described as core symptoms of MI. These can overlap with anxiety, anger, re-experiencing, self-harm, and social problems commonly seen in PTSD. The experiences of General (retired) Romeo Dallaire and other international experts who have led in times of crisis can help us better understand MI and recovery. Objectives: In honor of Dallaire, online opportunities were created for international students and leaders/experts to discuss topics of MI, stigma, and moral codes in times of adversity as well as the moral impact of war. We aimed to (1) better understand MI and moral dilemmas, and (2) identify key insights that could inform prevention of and recovery from MI. Materials and Methods: Webinars and conversations of 75-90 min duration on MI and recovery were facilitated by Leiden University, the University of Alberta and the Dallaire Institute for Children, Peace and Security between General Dallaire, world experts, and graduate students. Sessions were recorded, transcribed and thematically analyzed with NVivo using standard qualitative methodology. Results: = 43; female (19) and male (24)] from North America, Europe, Australia and the global south. Themes included: (1) recognizing the impact of exposure to PMIEs, (2) reducing stigma around MI, and (3) embracing the spiritual depth of humanity. Conclusion: Exposure to PMIEs can have devastating impacts on military members, leaders and other uniformed professionals. This may lead to development of MI and PTSD. Recognizing MI as honorable may reduce stigma and psychological harm, and facilitate help-seeking among uniformed personnel and other trauma-affected populations. Salient efforts to address MI must include use of accurate measurements of MI and integrated holistic therapeutic approaches, inclusive of spiritual and social components. Urgency remains regarding the prediction, identification and treatment of MI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".