Pre-existing depression, anxiety and trauma as risk factors for the development of post-traumatic stress disorder symptoms following wildfires
Bibliographic record
Abstract
The trauma of wildfires leads to one of the most challenging and treatment resistant mental health conditions-namely-post-traumatic stress disorder (PTSD). Research addressing the contribution of pre-existing mental health conditions to the development of PTSD symptoms following traumatization by wildfires is limited. This study examined whether people with pre-existing diagnoses of anxiety, depression, PTSD, insomnia and nightmares, by a mental health professional, are more likely to develop symptoms of PTSD than those with no previous diagnosis following the trauma of wildfires. A total of 126 wildfire survivors from Australia, Canada and the United States of America completed an online survey. An independent sample t-tests revealed that pre-existing diagnosed conditions of depression, an anxiety disorder and PTSD significantly increased the likelihood of developing PTSD symptoms following traumatization by wildfires (t = −2.51, p = 0.014, 95% CI [-18.91 – -2.20], t = −2.61, p = 0.01, 95% CI [-18.91 – -2.57], t = −2.57, p = 0.012, 95% CI [-22.36 – -2.87] respectively). Practitioners working in communities subjected to wildfires need to run a thorough screening of their patients’ pre-existing mental health conditions to provide the right treatment and referral pathways to those affected by the trauma of wildfires.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".