Psychological Symptoms Among Evacuees From the 2016 Fort McMurray Wildfires: A Population-Based Survey One Year Later
Bibliographic record
Abstract
Background: The 2016 wildfires in Fort McMurray (Alberta, Canada) led to a massive displacement of 88,000 people and destroyed 2,400 homes. Although no direct human fatality resulted, many individuals feared for their lives or those of their loved ones. Objectives: (1) To estimate the prevalence of post-traumatic stress, major depressive, insomnia, generalized anxiety, and substance use disorders in the adult population of Fort McMurray 1 year after the evacuation; (2) To identify pre-, peri-, and post-disaster correlates of mental health disorders. Methods: A phone survey using random digit sampling was used to survey evacuees. A total of 1,510 evacuees (response rate = 40.2%, 55.5% women, mean age = 44.11, SD = 12.69) were interviewed between May 9th and July 28th, 2017. Five validated scales were administered: the PTSD Symptoms Checklist (PCL-5), the Insomnia Severity Index (ISI), the depression and anxiety subscales of the Patient Health Questionnaire (PHQ-9, GAD-7), and the CAGE Substance Abuse Screening Tool. Results: One year after the wildfires, 38% had a probable diagnosis of either post-traumatic stress, major depressive, insomnia, generalized anxiety, or substance use disorder, or a combination of these. Insomnia disorder was the most common, with an estimated prevalence of 28.5%. Post-traumatic stress, major depressive and generalized anxiety disorders were almost equally prevalent, with ~15% each. The estimated prevalence of substance use disorder was 7.9%. For all five mental health disorders, having a mental health condition prior to the fires was a significant risk factor, as well as having experienced financial stress or strain due to the economic decline already present in Fort McMurray. Five post-disaster consequences were significant predictors of four of the five disorders: decrease in work, decrease in social life, poorer current health status, increase in drug and alcohol use, and higher level of stress experienced since the fires. Conclusion: One year after the fires, more than one third of the evacuees had clinically significant psychological symptoms, including those of insomnia, post-traumatic stress, depression, anxiety, and substance use. This study helped identify individuals more at risk for mental health issues after a natural disaster and could guide post-disaster psychosocial support strategies.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".