Defining and Measuring Moral Injury: Rationale, Design, and Preliminary Findings From the Moral Injury Outcome Scale Consortium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it