Psychometric Evaluation of the Moral Injury Events Scale in Two Canadian Armed Forces Samples
Bibliographic record
Abstract
Moral injury (MI) is defined as the profound psychological distress experienced in response to perpetrating, failing to prevent, or witnessing acts that transgress personal moral standards or values. Given the elevated risk of adverse mental health outcomes in response to exposure to morally injurious experiences in military members, it is critical to implement valid and reliable measures of MI in military populations. We evaluated the reliability, convergent, and discriminant validity, as well as the factor structure of the commonly used Moral Injury Events Scale (MIES) across two separate active duty and released Canadian Armed Forces samples. In Study 1, convergent and discriminant validity were demonstrated through correlations between MIES scores and depression, anxiety, posttraumatic stress disorder, anger, adverse childhood experiences, and combat experiences. Across studies, internal consistency reliability was high. However, dimensionality of the MIES remained unclear, and model fit was poor across active and released Canadian Armed Forces samples. Practical and theoretical implications are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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 teacher head, 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".