Defining and Measuring Moral Injury: Rationale, Design, and Preliminary Findings From the Moral Injury Outcome Scale Consortium
Bibliographic record
Abstract
In the current paper, we first describe the rationale for and methodology employed by an international research consortium, the Moral Injury Outcome Scale (MIOS) Consortium, the aim of which is to develop and validate a content-valid measure of moral injury as a multidimensional outcome. The MIOS Consortium comprises researchers and clinicians who work with active duty military service members and veterans in the United States, the United Kingdom, the Netherlands, Australia, and Canada. We describe the multiphase psychometric development process being conducted by the Consortium, which will gather phenomenological data from service members, veterans, and clinicians to operationalize subdomains of impact and to generate content for a new measure of moral injury. Second, to illustrate the methodology being employed by the Consortium in the first phase of measure development, we present a small subset of preliminary results from semistructured interviews and questionnaires conducted with care providers (N = 26) at three of the 10 study sites. The themes derived from these initial preliminary clinician interviews suggest that exposure to potentially morally injurious events is associated with broad psychological/behavioral, social, and spiritual/existential impacts. The early findings also suggest that the outcomes associated with acts of commission or omission and events involving others' transgressions may overlap. These results will be combined with data derived from other clinicians, service members, and veterans to generate the MIOS.
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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 | 0.158 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".