A Scoping Review on the Prevalence and Determinants of Post-Traumatic Stress Disorder among Military Personnel and Firefighters: Implications for Public Policy and Practice
Bibliographic record
Abstract
INTRODUCTION: Firefighters and military personnel are public safety personnel who protect the safety of individuals and their properties. They are usually exposed to traumatic events leaving them at risk of developing mental health conditions such as post-traumatic stress disorder (PTSD). Increasing concern is being raised regarding the mental health impacts, specifically PTSD, among military personnel and firefighters. OBJECTIVE: There is an increased exposure of firefighters and military personnel to traumatic events and the attendant risk of developing post-traumatic stress disorder. It is crucial to ascertain the level of PTSD amongst this cohort and determinants to formulate policies and practices that mitigate the risk and protect public safety personnel. This scoping review sought to determine the prevalence of PTSD among this cohort globally and to explore determinants of this mental health condition. METHODS: A literature search in databases including MEDLINE, CINAHL, PubMed, PsycINFO, and EMBASE was conducted electronically from May 2021 to 31 July 2021. Two reviewers independently assessed full-text articles according to the predefined inclusion criteria and screening process undertaken to identify studies for the review. Articles were screened with a third reviewer, resolving conflicts where necessary and further assessing them for eligibility. During article selection, the PRISMA checklist was adopted, and with the Covidence software, a total of 32 articles were selected for the final examination. For the eligible studies, data extraction was conducted, information was collated and summarized, and the findings were reported. Original qualitative and quantitative data on the prevalence and predictors of PTSD among veterans, military, and firefighters were reported. RESULTS: The prevalence of PTSD was 57% for firefighters and 37.8% for military personnel. Demographic factors, job factors, social support, injuries, physical and psychological factors, and individual traits were the main predictors of PTSD in this cohort. CONCLUSION: This information is vital for developing and implementing prevention and intervention strategies for PTSD in military personnel and firefighters. Recognizing and addressing factors that predict PTSD will help to improve mental wellbeing and increase productivity. More peer-reviewed studies are needed on the prevalence of PTSD amongst these cohorts.
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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 | 0.034 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.028 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".