Identifying potentially morally injurious events from the Veteran perspective: A qualitative descriptive study
Bibliographic record
Abstract
Introduction: Current conceptualizations of potentially morally injurious events (PMIEs) emphasize war atrocities. However, additional research on the breadth of PMIEs could inform provision of patient-centred care for those experiencing moral injury. This study sought to gain a more in-depth understanding of Veterans' experiences surrounding PMIEs. Methods: Semi-structured, in-depth individual interviews were conducted with 32 Veterans who agreed or strongly agreed that they witnessed, did not stop (despite believing they could have), did things they felt were morally wrong during their time in a war zone, or any combination of these. Participants were asked what is important to know about such events and probed to describe the events in whatever level of detail they felt comfortable. Applied thematic analysis was used to code and analyze the data, including structural and content coding. Coding discrepancies were resolved by mutual consensus. Results: In addition to war atrocities, analyses revealed types of events that may be overlooked as potentially morally injurious but that were salient to Veterans: 1) fraud, waste, and abuse, 2) animal cruelty, 3) bullying and reputation smearing, 4) infidelity, 5) racism and sexism, 6) morally abhorrent practices, and 7) events outside the military. Veterans also reported and described multiple events at once or over time given multiple deployments and time in service rather than identifying a single specific PMIE. Discussion: The field would benefit from an operationalization of PMIEs not only grounded in empirical data and meaningful to clinicians but that also accounts for the perspectives of the Veterans who experienced PMIEs.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".