Moral injury, coping strategies, and supports among Canadian public safety personnel
Bibliographic record
Abstract
Introduction: Public safety personnel (PSP) are at increased risk of exposure to potentially morally injurious events (PMIEs) and moral injury because of the nature of their work. The purpose of this study was to explore how moral injury relates to coping strategies and supports that PSP may use to care for their mental health. Method: Between June 2022 and June 2023, PSP were invited to participate in an online survey that indexed socio-demographic information, moral injury (with shame and trust violation sub-scales), perceived organizational support, social support, spiritual well-being, self-compassion, alcohol use, cannabis use, and childhood adversity. Hierarchical multiple linear regressions were constructed to assess the relations between coping strategies and supports and 1) overall moral injury, 2) shame-related moral injury, and 3) trust-violation-related moral injury, controlling for age, mental health history, and childhood adversity. Results: Moral injury was negatively associated with perceived organizational support, spiritual well-being, and self-compassion. Shame-related moral injury was negatively associated with spiritual well-being, self-compassion, and social support. Finally, trust-violation-related moral injury was positively associated with alcohol use and negatively associated with perceived organizational support and spiritual well-being. Discussion: Although PSP and related groups may continue to experience moral injury because of job-related duties, there may be room to intervene via spiritually informed resources, self-compassion training, and social and organizational support.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".