Effects of cumulative trauma from multiple natural disasters
Bibliographic record
Abstract
Introduction Fort McMurray, a city in northern Alberta, Canada, has experienced multiple traumatic events in the last five years, including the 2016 wildfire, the 2020 floods, and the COVID-19 pandemic. Traumatic events often lead to increased mental health burdens in affected communities. Objectives To assess if the number of traumatic events experienced by residents of Fort McMurray correlates with the prevalence and severity of mental health issues experienced. Methods A cross-sectional study using an online survey questionnaire was used to gather demographic, trauma (wildfire, flooding, and COVID-19), and clinical information from the resident of Fort McMurray between April 24 to June 2 2021. Likely Generalized Anxiety Disorder (GAD), Major Depressive Disorder (MDD), Post-Traumatic Stress Disorder (PTSD) and low resilience were measured using standardized rating scales. Data were analyzed with SPSS version 26 using Chi-Square tests and multivariate regression analysis. Results Respondents who experienced COVID-19 and either flood or wildfire traumas (N = 101) were eleven times more likely to have GAD symptoms (OR: 11.39; 95% CI: 1.43-91.04), four times more likely to have likely MDD, (OR: 3.85; 95% CI: .995-14.90), ten times more likely to have likely PTSD (OR: 10.47; 95% CI: 1.28-85.67), and low resilience (OR: 10.56; 95% CI: 1.21-92.17). Respondents who experienced COVID-19, flooding, and wildfire traumas (N = 47) were eighteen times more likely to express GAD symptoms (OR: 18.30; 95% CI: 2.20-152.45) and more than eleven times likely to have likely PTSD (OR: 11.41; 95% CI: 1.34-97.37) in comparison to the respondents who experienced COVID-19 only trauma (N = 19). Conclusions Measures to reduce climate change and associated natural disasters could reduce the impact of cumulative trauma and associated mental health burden in vulnerable populations. It is essential that more mental health resources are mobilized to support communities impacted by multiple natural disasters. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".