One Year after the Flood: Prevalence and Correlates of Post-Traumatic Stress Disorder among Residents in Fort McMurray
Bibliographic record
Abstract
BACKGROUND: The 2020 Fort McMurray (FMM) and area flood caused more than $228 million in insured damage, affected over 1200 structures, and more than 13,000 people were evacuated. OBJECTIVE: This study sought to determine the prevalence of post-traumatic stress disorder (PTSD)-like symptoms and the risk predictors among the population of FMM one year after the 2020 flooding. METHODS: An online quantitative cross-sectional survey was distributed to residents of FMM via REDCap between 24 April to 2 June 2021 to collect sociodemographic, clinical, and flood-related information. The PTSD checklist for DSM-5 (PCL-C) was used to assess likely PTSD among respondents. RESULTS: 186 of 249 respondents completed all essential self-assessment questionnaires in the analysis, yielding a response rate of 74.7%. The prevalence of likely PTSD was 39.6% (65). Respondents with a history of depression were more likely to develop PTSD symptoms (OR = 5.71; 95% CI: 1.68-19.36). Similarly, responders with limited and no family support after the disaster were more prone to report PTSD symptoms ((OR = 2.87; 95% CI: 1.02-8.05) and (OR = 2.87; 95% CI: 1.06-7.74), respectively). CONCLUSIONS: Our research indicated that history of depression and the need for mental health counseling significantly increased the risk of developing PTSD symptoms following flooding; family support is protective. Further studies are needed to explore the relations between the need to receive counseling and presenting with likely PTSD symptoms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".