Moral injury symptoms and related problems among service members and Veterans: A network analysis
Bibliographic record
Abstract
Introduction: Whether moral injury (MI) is distinct from posttraumatic stress disorder (PTSD) has been debated. Both result from events that often definitionally overlap (a potentially morally injurious event [PMIE] for MI, a Criterion A event for PTSD) and may promote similar dysfunctional experiences. Depressive symptoms may also follow such events and include outcomes common to both MI and PTSD. This study investigated the ways in which MI may be distinct from, and related to, PTSD and depression by examining networks consisting of MI-related outcomes (trust violation, shame, functioning), PTSD symptom clusters, and depression among those who reported experiencing a PMIE and those who did not. Methods: Two networks were estimated, consisting of PTSD symptoms, MI-shame-related outcomes, MI-trust-related outcomes, MI-related functioning, and depression in a sample of military personnel who did (n = 508) and did not (n = 123) experience a PMIE. Results: In both PMIE and non-PMIE networks, stronger connections existed within, versus across, constructs. The PMIE network was denser than the non-PMIE network and driven by more connections across constructs. Negative alterations in cognitions and mood (NACM) clusters of PTSD and MI-related functioning were strong bridges connecting PTSD, MI, and depression. Discussion: MI, PTSD, and depression appear to be distinct but related clinical phenomena. NACM and MI-related functioning partially explain the co-occurrence in these constructs and thus may be important treatment targets. The greater connections across constructs in the PMIE network supports the hypothesis that experiencing a PMIE may trigger dynamic interactions among PTSD, MI-related outcomes, and depression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".