Community resilience and associated factors in Fort McMurray a year after the devastating flood
Bibliographic record
Abstract
Introduction A natural disaster like flooding causes loss of properties and evacuation and effective mental health. Resilience after natural disasters is a crucial area of research which needs attention. Objectives To explore the prevalence and associated factors of low resilience a year after the 2020 floods in Fort McMurray. Methods A cross-sectional study was conducted in Fort McMurray using online surveys. The data were analyzed with SPSS version 25 using univariate analysis with the chi-squared test and binary logistic regression analysis. Results The prevalence of low resilience was 37.4%. Respondents under 25 years were nearly 26 times more likely to show low resilience (OR= 0.038; 95% CI 0.004 - 0.384). Responders with a history of depression and anxiety (OR= 0.212; CI 95% 0.068-0.661) were nearly four to five times more likely to show low resilience. Similarly, respondents willing to receive mental health counselling (OR=0.134 95%CI: 0.047-0.378) were 7.5 times more likely to show low resilience. Participants residing in the same house before the flood were almost 11 times more likely to show low resilience (OR=0.095; 95% CI 0.021- 0.427), and support from the Government of Alberta was a protective factor. Conclusions The study showed demographic, clinical, and flood-related variables contributing to low resilience. Receiving support from the Government was shown to be a protective factor against low resilience. More robust measures must be in place to promote normal to high resilience among flood victims in affected communities. Disclosure of Interest None Declared
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".