Greater prevalence of post‐traumatic stress disorder and depression in deployed Canadian Armed Forces personnel at risk for moral injury
Bibliographic record
Abstract
BACKGROUND: A link between moral injury (i.e., the psychological distress caused by perceived moral transgressions) and adverse mental health outcomes (AMHO) has been recently proposed. However, the prevalence of exposure to morally injurious events and the associated risk of experiencing AMHO remains understudied. METHOD: The impact of exposure to potentially morally injurious experiences (PMIEs) was explored in relation to past-year PTSD and MDD, using the 2013 Canadian Armed Forces Mental Health Survey dataset of Afghanistan mission deployed regular force and reserve personnel. A series of logistic regressions were conducted, controlling for relevant sociodemographic, military, deployment, and trauma-related variables. RESULTS: Over half of the deployed personnel endorsed at least one PMIE. Several demographic and military variables were associated with exposure to PMIEs. Those exposed to PMIEs demonstrated a greater likelihood of having past-year PTSD and MDD; feeling responsible for the death of Canadian or ally personnel demonstrated the strongest association with PTSD and MDD. Mental health training was not a moderator for PMIE exposure and AMHO. CONCLUSIONS: Exposure to PMIEs during deployments is common and represents an independent risk factor for past-year PTSD and MDD. Improved training that targets moral-ethical dilemmas and treatment interventions that address moral injury expressions is warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".