Long-Term Mental Health Effects of a Devastating Wildfire Are Amplified by Sociodemographic and Clinical Antecedents in College Students
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to assess prevalence of major depressive disorder (MDD), generalized anxiety disorder (GAD), and posttraumatic stress disorder (PTSD) in students of Keyano College 18 months after a wildfire and to determine the predictors of likely MDD, GAD, and PTSD in the respondents. METHODS: A quantitative cross-sectional survey was used to collect data through self-administered, paper-based questionnaires to determine likely MDD, GAD, and PTSD using the PHQ 9, GAD-7, and the PTSD Checklist for DSM 5, Part 3, respectively. Data were analyzed with SPSS version 20 (IBM Corp, Armonk, NY) using univariate analysis with chi-square tests. RESULTS: Eighteen months after the wildfire, the 1-month prevalence rates for MDD, GAD, and PTSD among the college students were 23.4%, 18.7%, and 11.0%, respectively. There were statistically significant associations between multiple sociodemographic variables and the likelihood respondents presented with MDD, GAD, and PTSD 18 months after the wildfire. There were also associations between the likely MDD, GAD, and PTSD and abuse/dependence on alcohol and substances in respondents at 18 months. CONCLUSION: Our study has established prevalence rates for MDD, GAD, and PTDS among college students 18 months after the Fort McMurray wildfires. Further studies are needed to explore the impact of college-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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".