The Impact of Wildfires on Mental Health: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the many consequences of climate change is an increase in the frequency, severity, and, thus, impact of wildfires across the globe. The destruction and loss of one's home, belongings, and surrounding community, and the threat to personal safety and the safety of loved ones can have significant consequences on survivors' mental health, which persist for years after. The objective of this scoping review was to identify primary studies examining the impact of wildfires on mental health and to summarize findings for PTSD, depression, anxiety, and substance use. Literature searches on Pubmed and Embase were conducted in February and April of 2021, respectively, with no date restrictions. A total of 254 studies were found in the two database searches, with 60 studies meeting the inclusion criteria. Three other studies were identified and included based on relevant in-text citations during data abstraction. The results show an increased rate of PTSD, depression, and generalized anxiety at several times of follow-up post-wildfire, from the subacute phase, to years after. An increased rate of mental health disorders post-wildfire has been found in both the adult and pediatric population, with a number of associated risk factors, the most significant being characteristics of the wildfire trauma itself. Several new terms have arisen in the literature secondary to an increased awareness and understanding of the impact of natural disasters on mental health, including ecological grief, solastalgia, and eco-anxiety. There are a number of patient factors and systemic changes that have been identified post-wildfire that can contribute to resilience and recovery.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it