Mental Disorders in Firefighters Following Large-Scale Disaster
Bibliographic record
Abstract
Firefighting service is known to involve high rates of exposure to potentially traumatic situations, and research on mental health in firefighting populations is of critical importance in understanding the impact of occupational exposure. To date, the literature concerning prevalence of trauma-related mental disorders such as posttraumatic stress disorder (PTSD) has not distinguished between symptomology associated routine duty-related exposure and exposure to large-scale disaster. The present systematic review synthesizes a heterogeneous cross-national literature on large-scale disaster exposure in firefighters and provides support for the hypothesis that the prevalence of PTSD, major depressive disorder, and anxiety disorders are elevated in firefighters compared with rates observed in the general population. In addition, we conducted narrative synthesis concerning several commonly assessed predictive factors for disorder and found that sociodemographic factors appear to bear a weak relationship to mental disorder, while incident-related factors, such as severity and duration of disaster exposure, bear a stronger and more consistent relationship to the development of PTSD and depression in cross-national samples. Future work should expand on these preliminary findings to better understand the impact of disaster exposure in firefighting personnel.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".