Interventions for the prevention and management of occupational stress injury in first responders: a rapid overview of reviews
Bibliographic record
Abstract
BACKGROUND: First responders are a high-risk population for occupational stress injuries as they often encounter prolonged stress within their line of work. The aim of this rapid overview of reviews is to summarize existing evidence on interventions for the prevention and management of occupational stress injury (OSI) in first responders. METHODS: MEDLINE, EMBASE, PsycINFO, CINAHL, Web of Science, and Cochrane Library were searched for systematic reviews examining the impact of prevention, rehabilitation, and resilience-building strategies targeting frontline community safety personnel in February 2019. Pairs of reviewers screened titles and abstracts followed by full-text articles and conducted data abstraction and quality appraisal using the AMSTAR II tool. To ensure a rapid overview process, the search strategy was limited to the last 10 years, quality appraisal of reviews and abstraction of study-level data was completed by one person and verified by another, and the quality of the individual primary studies was not appraised. The findings were summarized descriptively. RESULTS: A total of 14 reviews with 47 unique primary studies were found after screening 1393 records. A majority of studies targeted OSI in police officers (78.7%), followed by firefighters (17%) and correctional officers (4.3%). Of the 47 included primary studies, 24 targeted prevention of OSI (i.e., resilience training, stress management, suicide prevention, and other health promotions) and 23 targeted rehabilitation (i.e., drug therapy, psychotherapy, and other therapies). Prevention strategies including resilience training programs had positive outcomes, while suicide prevention and psychotherapy interventions reported mixed results. CONCLUSIONS: Some promising interventions targeting the prevention and rehabilitation of OSI among police officers, firefighters, and correctional officers were identified in the included studies, and these results will serve as a basis for the development of evidence-based strategies to mitigate future risks in this population. However, several gaps were also identified in this area that will require further investigation prior to widespread implementation of effective interventions. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019125945.
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".