Negative cognitions in the context of suicidality after exposure to military-related potentially morally injurious events
Bibliographic record
Abstract
Introduction: Consistent associations are found between exposure to potentially morally injurious events (PMIEs) and suicidal thoughts and behaviours (STBs). Studies examining cognitions after PMIEs have focused on those describing one's role in the event (e.g., "I did something wrong") rather than those describing sense of self (e.g., "I am a bad person"). Stable internal attributions (e.g., "I did a bad thing because I'm a bad person") are shown to be particularly psychologically harmful; therefore, this is an important limitation. The study explored differences in negative cognitions in those reporting and not reporting STBs after a PMIE. Methods: This study is a secondary analysis of data from a treatment-seeking sample of Canadian military members and Veterans reporting distress after a PMIE (N = 55). Participants completed clinician-administered interviews on STBs and questionnaires measuring negative cognitions. Results: More than half (n = 30) of participants endorsed current STBs, and 20.0% (n = 11) reported previously attempting suicide. There were no differences in cognitions related to hindsight/responsibility, wrongdoing, lack of justification, self-blame, or negative cognitions about the world. Participants with STBs reported more negative cognitions about the self than those without current STBs. Discussion: Results suggest that negative cognitions focusing on stable features of the self, rather than on events, may be more important in their association with STBs after a PMIE. This is consistent with the notion that shame, rather than guilt, is more problematic in psychopathology and STBs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".