The Psychological Vest: Trauma, Resiliency, and Posttraumatic Growth Among Police Officers
Bibliographic record
Abstract
Police officers are often required to make split-second decisions in unpredictable and ambiguous critical situations, while held to extremely high moral and ethical standards and intense public scrutiny. Given their level of trauma exposure and risk for traumatic and morally injurious distress, it is vital to better understand psychosocial factors which serve to increase risk or resilience, to shape a metaphoric psychological vest. In addition, a psychometrically sound measure of moral injury is needed to accurately identify such risk and resiliency factors. The current dissertation project first investigated the psychometric properties of the Moral Injury Assessment for Public Safety Personnel (MIA-PSP). Next, thematically-connected psychological (i.e., facets of mattering, grit, socially prescribed perfectionism, self-compassion, posttraumatic cognitions) and social (i.e., workplace stress, job satisfaction, childhood adversity, social support, perceived public benevolence) factors were examined in their relatedness to posttraumatic stress disorder (PTSD), moral injury (MI), depression, anxiety, burnout, life satisfaction, and posttraumatic growth. The sample of study were 367 police officers (Median = 20 years of service; 72.5% men) from 17 small to large municipal and provincial police services across Ontario. Officers completed an online battery of validated measures assessing both the aforementioned risk and resiliency factors and trauma-related outcomes. First, regarding the MIA-PSP, confirmatory factor analysis modelling supported a correlated three-factor structure that was invariant across gender and years of service. Controlling for shared variance amongst the subscales, the emotional sequelae and betrayals subscales demonstrated unique predictive power with measures of trauma, trauma-related outcomes, and well-being. Findings suggest the MIA-PSP is a promising scale to assess MI within police populations. Second, the psychosocial factors of anti-mattering, self-compassion, and posttraumatic cognitions were identified as predictive of every distressing trauma-related outcome under investigation. Heightened anti-mattering and posttraumatic cognitions served as risk factors for increased PTSD, MI, depression, anxiety, burnout and poorer life satisfaction, with heightened self-compassion serving as a resilience factor in buffering against those outcomes and facilitating life satisfaction and posttraumatic growth. These risk and resilience factors are posited as tied to a core emotion of shame, which is discussed with reference to notable opportunities for clinical intervention.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".