A systematic review of mental health symptoms in police officers following extreme traumatic exposures
Bibliographic record
Abstract
In addition to high-risk and high-stress events that police officers routinely encounter, many are also exposed to extreme traumatic exposures or disasters caused by nature (eg. Hurricanes) and human action (e.g., terrorist attacks or plane crashes). These exposures can result in a variety of adverse reactions including post-traumatic stress disorder (PTSD), acute stress disorder, major depressive disorder and anxiety disorders. Understanding and accurately measuring the burden of disease arising from involvement in extreme events, on policing organizations and individual police officers is critical for policy makers and those who plan and deliver services. This systematic review synthesizes existing research on large-scale disasters, in order to further our understanding of how extreme events impact the mental health of police officers. The results found variability in the reported rates of mental disorder; however, there are some clear trends. Overall, the rates of PTSD among police officers that are consistently lower than those of civilians affected by the same disaster, and are lower than other occupations. This undoubtedly speaks to the resilience and training of members of policing organizations that prepare them for this work. Studies also demonstrate that reported distress in terms of acute stress disorder, anxiety and depression, continues to rise in some groups as time-elapsed from the event lengthens; suggesting a need to ensure that mental health supports are provided at later stages after the event.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".