Evaluating the Prevalence and Correlates of Major Depressive Disorder Among Residents of Fort McMurray, Canada, One Year After a Devastating Flood
Bibliographic record
Abstract
Abstract Objective: This study assessed the prevalence and correlates of depression following the April 2020 flooding in Fort McMurray. Methodology: A cross-sectional study design. Questionnaires were self-administered through an anonymous, online survey. Data collected included sociodemographics, flooding-related variables, clinical information, and likely major depressive disorder (MDD) using PHQ-9 scoring. Data were analyzed using descriptive statistics, the chi-square test, and logistic regression at P = < 0.05. Results: Of the 186 respondents who completed the survey, 85.5% (159) of the respondents were females, 14.5% (27) were males, 52.7% (98) were above 40 years of age, and 94% (175) were employed. The prevalence of mild to severe depression among the respondents was 53.7% (75). Respondents who reported that they are unemployed are 12 times more likely to have a moderate to severe depression (OR = 12.16; 95% CI: 1.08–136.26). Respondents who had previously received a mental health diagnosis of MDD are five times more likely to have moderate to severe depression (OR = 5.306; 95% CI: 1.84–15.27). Conclusion: This study suggests that flooding could impact the psychosocial and mental health of affected people. There is a need to reassess the existing guidelines on emergency planning for flooding to reduce its impacts on mental health and identify where research can support future evidence-based guidelines.
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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) | 0.001 | 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".