Defining and Assessing the Syndrome of Moral Injury: Initial Findings of the Moral Injury Outcome Scale Consortium
Bibliographic record
Abstract
Potentially morally injurious events (PMIEs) entail acts of commission (e.g., cruelty, proscribed or prescribed violence) or omission (e.g., high stakes failure to protect others) and bearing witness (e.g., to grave inhumanity, to the gruesome aftermath of violence), or being the victim of others' acts of commission (e.g., high stakes trust violations) or omission (e.g., being the victim of grave individual or systemic failures to protect) that transgress deeply held beliefs and expectations about right and wrong. Although there is a proliferation of interest in moral injury (the outcome associated with exposure to PMIEs), there has been no operational definition of the putative syndrome and no standard assessment scheme or measure, which has hampered research and care in this area. We describe an international effort to define the syndrome of moral injury and develop and validate the Moral Injury Outcome Scale (MIOS) in three stages. To ensure content validity, in Stage I, we conducted interviews with service members, Veterans, and clinicians/Chaplains in each country, inquiring about the lasting impact of PMIEs. Qualitative analysis yielded six operational definitions of domains of impact of PMIEs and components within domains that establish the parameters of the moral injury syndrome. From the domain definitions, we derived an initial pool of scale items. Stage II entailed scale refinement using factor analytic methods, cross-national invariance testing, and internal consistency reliability analyses of an initial 34-item MIOS. A 14-item MIOS was invariant and reliable across countries and had two factors: Shame-Related (SR) and Trust-Violation-Related (TVR) Outcomes. In Stage III, MIOS total and subscale scores had strong convergent validity, and PMIE-endorsers had substantially higher MIOS scores vs. non-endorsers. We discuss and contextualize the results and describe research that is needed to substantiate these inaugural findings to further explore the validity of the MIOS and moral injury, in particular to examine discriminant and incremental validity.
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".