Measuring Moral Injury and Its Impacts in Canadian Armed Forces and Public Safety Personnel
Bibliographic record
Abstract
Military service has been previously identified as a risk factor for adverse mental health outcomes, including posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and suicidal ideation. Existing literature confirms the heightened prevalence of these conditions among armed forces and public safety personnel (PSP) compared to the general population. However, to date there is limited research regarding the interplay between moral injury (MI) and other mental health conditions in Canadian Armed Forces (CAF) and PSP. Therefore, this body of research investigates the intersection of potentially morally injurious experiences (PMIEs) and mental health outcomes among CAF and PSP, contributing to the growing literature on MI. The dissertation includes three studies presented as three separate articles which have undergone peer-review and publication. Collectively, through mixed-method investigation, the three studies highlight the pervasive impact of PMIEs on mental health outcomes among CAF and PSP populations. In the first study, I qualitatively explore the experiences of CAF members and PSP, highlighting the complex interplay between professional duties and personal well-being, while deepening our understanding of the subjective experiences of morally injurious experiences. Building upon these insights, the second study quantitatively identifies risk factors of moral injury in CAF personnel, including stressful deployment experiences, sexual trauma, and childhood maltreatment. Lastly, the third study examines the associations between moral injury and mental health disorders among CAF personnel and Veterans. Robust relations were found between MI and various mental health conditions, emphasizing the need for comprehensive support systems and tailored interventions to address these complex interplays. Together, these findings elucidate the importance of identifying and addressing the distinct challenges faced by CAF and PSP, to promote well-being and resilience in high-stress occupational environments.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".