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 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 | 0.001 | 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".