Reducing the impacts of exposure to potentially traumatic events on the mental health of public safety personnel: A rapid systematic scoping review.
Bibliographic record
Abstract
Police, firefighters, and emergency medical technicians and paramedics are frequently exposed to potentially traumatic events (PTE) in their work as public safety personnel (PSP). PTE are a risk factor for posttraumatic stress disorder, depression, anxiety, substance abuse, and suicidal ideation. This systematic rapid scoping review summarizes evaluation research on psychosocial interventions to reduce the negative consequences of exposure to work-related PTE on the mental health of PSP. Articles were identified using PubMed and PsycInfo. Publications from January 1, 2013, to December 1, 2020, were retained because the research published before 2013 was covered by other reviews. We identified 601 unique documents; 30 met preliminary eligibility criteria; and 18 were retained. Most studies were limited to police officers and firefighters. Participants exposed to prevention measures reported improvements of symptoms of depression, burnout, anxiety, sleep problems, and well-being. Most articles identified factors that may hinder or facilitate the use of program components. Studies on ways to support PSP recently affected by a PTE are lacking. There are little data on the efficacy of prevention programs in reducing the prevalence of mental disorders associated with PTE. More studies should focus on identifying participant characteristics and intervention components that influence program use, adherence, and efficacy. Realistic evaluations combined with participatory research could help address important knowledge gaps. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".