Post-Traumatic Stress Disorder in Fort McMurray: Prevalence and Correlates One Year After the Flood
Bibliographic record
Abstract
INTRODUCTION: As a result of the floods in Fort McMurray (FMM) and the surrounding area in 2020, more than $228 million in insured damage was sustained, over 1200 structures were damaged, and more than 13,000 people were evacuated from their homes. OBJECTIVES: The aim of the study was to determine the prevalence of post-traumatic stress disorder (PTSD)-like symptoms among the population of FMM one year after the flood in 2020 and the risk predictors associated with such symptoms. METHODS: In order to collect sociodemographic, clinical, and flood-related information from residents of FMM via REDCap, an online quantitative cross-sectional survey was administered between April 24 and June 02, 2021. Respondents were assessed for probable PTSD using the PTSD Checklist for DSM-5 (PCL-C). RESULTS: An overall response rate of 74.7% was achieved among 186 of 249 respondents. The prevalence of likely post-traumatic stress disorder was 39.6% (65). There was a significantly increased likelihood of developing PTSD symptoms in respondents with a history of depression (OR= 5.71; 95% CI: 1.68 - 19.36). As well, disaster responders experiencing limited and no family support after the disaster were more likely 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: As a result of our study, we found that those who had a history of depression and had sought health counseling were significantly more likely to develop PTSD symptoms following flooding, while those with family support were less likely to suffer from PTSD symptoms. There is a need for further studies to investigate the relationship between the need for counseling and the presentation of potential symptoms of post-traumatic stress disorder. 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 | 0.000 | 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 | 0.000 |
| 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".