Five Years after the Fort McMurray Wildfire: Prevalence and Correlates of Low Resilience
Bibliographic record
Abstract
BACKGROUND: The Fort McMurray wildfire of 3 May 2016 was one of the most devastating natural disasters in Canadian history. Although resilience plays a crucial role in the daily functioning of individuals by acting as a protective shield that lessens the impact of disasters on their mental well-being, to date little is known about the long-term impact of wildfires on resilience and associated predictors of low resilience. OBJECTIVES: The objective of the study was to assess the prevalence and predictors of resilience among residents of Fort McMurray five years after the wildfires. METHOD: This was a quantitative cross-sectional study. A self-administered online survey which included standardized rating scales for resilience (BRS), anxiety (GAD-7), depression (PHQ-9), and post-traumatic stress disorder (PTSD)(PCL-C) was used to determine the prevalence of resilience as well as its demographic, clinical, and wildfire-related predictors. The data were collected between 24 April and 2 June 2021 and analyzed using the Statistical Package for Social Sciences (SPSS) version 25 using univariate analysis with a chi-squared test and binary logistic regression analysis. RESULTS: A total of 186 residents completed the survey out of 249 who accessed the online survey, producing a response rate of 74.7%. Most of the respondents were females (85.5%, 159), above 40 years of age (81.6%, 80), employed (94.1%, 175), and in a relationship (71%, 132). Two variables-having had PTSD symptoms (OR = 2.85; 95% CI: 1.06-7.63), and age-were significant predictors of low resilience in our study. The prevalence of low resilience in our sample was 37.4%. CONCLUSIONS: Our results suggest that age and the presence of PTSD symptoms were the independent significant risk factors associated with low resilience five years after the Fort McMurray wildfire disaster. Further research is needed to enhance understanding of the pathways to resilience post-disaster to identify the robust predictors and provide appropriate interventions to the most vulnerable individuals and communities.
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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 | 0.002 |
| 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 | 0.001 | 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".