Exposure to Wildfires Exposures and Mental Health Problems among Firefighters: A Systematic Review
Bibliographic record
Abstract
Firefighters are severely impacted by climate events, yet they are an underexamined population with regard to climate change research. This systematic review aims to synthesize the existing literature on the psychological effects of wildfire events on firefighters and to discuss some of the major gaps in disaster research relating to first responders and their mental health. A thorough search of the existing literature through June 2023 on the topic of wildfires and first responder psychological health was conducted through the databases PubMed, PsychINFO, and Embase. This search yielded 13 final studies which met the exclusion and inclusion criteria for this review. The final studies consisted of populations that responded to wildfire events from four different countries (two from Israel, one from Canada, two from Greece, and eight from Australia). The data gathered by this review suggest that firefighters may experience many environmental and occupational exposures during wildfire suppression, which are linked to an increased risk of PTSD and other psychological symptoms even months after the event. This review brings to light the need for further research of the compounded effect of the environmental and psychological exposures of first responders and the potential psychological effects of those exposures.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".