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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".