Psychological effects of PTSD and major depression following the wildfires in Fort McMurray: A fifth-year post-disaster study
Bibliographic record
Abstract
INTRODUCTION: As a result of the wildfire that engulfed Fort McMurray (FMM), Alberta, Canada in May 2016, over 90,000 residents were evacuated from the city. Approximately 2400 homes, or 10% of the housing stock, were destroyed in Fort McMurray. About 200,000 hectors of forest were destroyed by the fire, which reached into Saskatchewan. In the aftermath of a major disaster, a community’s infrastructure is disrupted, and psychological, economic, and environmental effects can last for many years. OBJECTIVES: Intensive research was conducted in Fort McMurray five years after the wildfire disaster to determine the prevalence of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) among residents of the community and to determine the demographic, clinical, and other risk factors of probable MDD and PTSD. METHODS: An online questionnaire administered via REDCap was used to collect data in a quantitative cross-sectional study between 24 April and 2 June 2021. Patients were asked to complete the Patient Health Questionnaire (PHQ-9) in order to assess the presence of symptoms associated with MDD. An assessment of likely PTSD in respondents was conducted using the PTSD Checklist for DSM-5 (PCL-C). In this study, descriptive, univariate, and multivariate regression analyses were conducted. RESULTS: Out of 249 people who accessed the survey link, 186 completed it (74.7% response rate). There was a median age of 42 among the subscribers. A majority of the sample consisted of 159 (85.5%) females; 98 (52.7%) over the age of 40; 136 (71%) in a relationship; and 175 (94.1%) employed. Our study sample had an overall prevalence of 45.0% (76) of MDD symptoms. The multivariate logistic regression model revealed four variables that were independently associated with MDD symptoms, including being unemployed, diagnosed with MDD, taking sedative-hypnotics, and willingness to receive mental health counseling. A total of 39.6% of our respondents (65) reported having likely PTSD. Three independent variables: received a mental health depression diagnosis from a health professional, would like to receive mental health counseling, and have only limited or no support from familycontributed significantly to the model for predicting likely PTSD among respondents while controlling the other factors in the regression model. CONCLUSIONS: The findings of this study indicate that unemployment, the use of sleeping pills, the presence of a previous depression diagnosis, and the willingness to seek mental health counseling are significant factors associated with the increase in the prevalence of MDD and PTSD following wildfires. Support from family members may prevent these conditions from developing. DISCLOSURE OF INTEREST: None Declared
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".