Exploring the association between moral injury and posttraumatic stress symptoms among Canadian public safety personnel
Bibliographic record
Abstract
Public safety personnel (PSP), such as police officers, firefighters, correctional workers, and paramedics, routinely face work stressors that increase their risk of developing posttraumatic stress disorder (PTSD). PSP may additionally face moral transgressions in the workplace (e.g., witnessing human suffering, working within broken systems), heightening the risk of moral injury (MI) in this population. Research among military personnel and health care workers shows an association between MI and PTSD; however, less is known about the association between these constructs among PSP. Canadian PSP completed an online survey between June 2022 and June 2023, including a demographic questionnaire and measures of PTSD, MI, dissociation, depression, anxiety, stress, and childhood adversity. Latent variable structural equation modeling (SEM) was performed to ascertain the impact of a latent MI construct (i.e., shame, trust violation, functional impairment) on a latent PTSD construct (i.e., intrusions, avoidance, negative alterations in cognition and mood, hyperreactivity, depersonalization, derealization). Sex, age, depression, anxiety, stress, and childhood adversity were included as covariates. A total of 314 PSP were included in the data analysis. A latent variable SEM regressing PTSD onto MI and including covariates accounted for 83.7% of the variance in PTSD. MI was the strongest predictor compared to all covariates and was significantly associated with PTSD symptoms, β = .506, p < .001, above and beyond the impacts of sex, age, depression, anxiety, stress, and childhood adversity. These findings are consistent with research among military members and health care providers and highlight the importance of further exploring MI among PSP.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".