Firefighters and traumatic stress: a review
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a review of the literature with respect to traumatic stress in a firefighting context. The goal was to provide a clear and concise review intended for use by both researchers and practitioners. Firefighters are an under-researched group in the academic literature and updated review articles are necessary to advance this body of work. Design/methodology/approach Searches of the English language literature on firefighters and traumatic stress were completed and supplemented with a review of clinical information related to post-traumatic stress disorder (PTSD). The search was comprehensive but was not guided by systematic review guidelines. Findings Research regarding firefighters and traumatic stress is limited and inconsistent in outcome. Much of the available literature supports a link between fire service work and increased post-traumatic symptomatology; however, some research has neglected to demonstrate a relationship for these factors. Some efforts such as the training of coping skills and team building may improve firefighters’ resilience to PTSD, while therapeutic and psychopharmacological treatments may be effective in reducing PTSD progression. Originality/value To the authors’ knowledge, no previous paper is available that has specifically intended to address firefighters and traumatic stress in a review format for researchers and practitioners. In the authors’ experience, fire service members are eager for literature addressing their profession and presented in a manner accessible for both non-academic and academic audiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".