Mental Health Effects in Primary Care Patients 18 Months After a Major Wildfire in Fort McMurray: Risk Increased by Social Demographic Issues, Clinical Antecedents, and Degree of Fire Exposure
Bibliographic record
Abstract
Objectives: To assess prevalence of likely Post-Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), and Generalized Anxiety Disorder (GAD) in patients attending the only out-of-hours primary care clinic in Fort McMurray some 18 months following a major fire. Methods: A quantitative cross-sectional survey was used to collect data through self-administered paper-based questionnaires to determine likely PTSD, MDD and GAD using the PTSD Checklists for DSM 5, PHQ 9 and GAD-7 respectively from residents of Fort McMurray who were impacted by the wildfires. This was carried out eighteen (18) months after a major wildfire which required the rapid evacuation of the entire city population (approximately 90,000 individuals). Results: We achieved a response rate of 48% and results from the 290 respondents showed the one month prevalence rates for likely PTSD was 13.6%, likely MDD was 24.8%, and likely GAD was 18.0%. Compared to self-reported prevalence rates before the wildfire (0%, 15.2%, and 14.5% respectively) these were increased for all diagnoses. After controlling for other factors in a logistic regression model, there were statistically significant associations between individuals who had likely PTSD, MDD and GAD diagnoses and multiple socio-demographic, clinical, and exposure-related variables as follows:. PTSD: History of Anxiety Disorder and received counselling had Odds Ratios (ORs) of 5.80 and 7.14 respectively. MDD: Age, witnessed the burning of homes, history of Depressive Disorder and receiving low level support from friends and family had ORs of 2.08, 2.29, 4.63 and 2.5 respectively. GAD: Fearful for their lives or the lives of friends/family, history of Depressive Disorder, and history of Anxiety Disorder had odds ratios of 3.52, 3.04, and 2.68 respectively. There were also associations between individuals with a likely psychiatric diagnosis and those who also had likely alcohol or drug abuse/dependence. Conclusion: Our study suggests there are high prevalence rates for mental health and addiction conditions in patients attending the out-of-hours clinic 18 months after the wildfires, with significant associations between multiple variables and likely PTSD, MDD and GAD. Further studies are needed to explore the impact of population based mental health interventions on the long term mental health effects of the 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".