Patterns of distress associated with exposure to potentially morally injurious events among Canadian Armed Forces service members and Veterans: A multi‐method analysis
Bibliographic record
Abstract
OBJECTIVE: This study describes patterns of distress associated with exposure to potentially morally injurious experiences (PMIEs) in a Canadian military sample. METHOD: Thematic analysis was performed on interviews from PMIE-exposed military members and Veterans. Participants also completed structured diagnostic interviews, and measures of trauma exposure and psychopathology. Multiple regression examined associations among these variables. Information on pharmacological treatment and past diagnoses are reported. RESULTS: Eight qualitative themes were identified: changes in moral attitudes, increased sensitivity and reactivity to moral situations, loss of trust, disruptions in identity, disruptions in spirituality, disruptions in interpersonal relatedness, rumination, and internalizing and externalizing emotions and behaviors. Self-report data revealed that degree of PMIE exposure was meaningfully associated with posttraumatic stress disorder. CONCLUSION: Qualitative but not quantitative findings supported existing models of moral injury (MI). Additional research is needed to examine the impact of PMIE type on mental health, and to test basic assumptions of MI theory.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".