Devastating Wildfires and Mental Health: Major Depressive Disorder Prevalence and Associated Factors among Residents in Alberta and Nova Scotia, Canada
Bibliographic record
Abstract
Background: Since March 2023, hundreds of fires have burned from coast to coast throughout the country, placing Canada on track to have the worst wildfire season ever recorded. From East to West, provinces such as Quebec, Ontario, Nova Scotia, Alberta, and British Columbia have been particularly affected by large and uncontrollable wildfires. Objectives: The objective of this study was to determine the prevalence of depression symptoms and predictors among residents living in extreme climate conditions during the Canadian wildfires of 2023 in Alberta and Nova Scotia and to update the literature with data related to those wildfires. Methods: A cross-sectional quantitative survey was conducted in this study. REDCap was used to administer an online survey between 14 May and 23 June 2023. Through the Text4Hope program, participants subscribe to receive supportive SMS messages daily. As part of the initial welcome message, participants were invited to complete an online questionnaire, containing demographic information, wildfire-related information, and responses to the Patient Health Questionnaire-9 (PHQ-9) for depression assessment. SPSS version 25 was used to analyze the data. Descriptive, univariate, and multivariate regression analyses were employed. Results: A total of 298 respondents completed the survey out of 1802 who self-subscribed to the Text4Hope program in Alberta and Nova Scotia and received a link to the online survey, producing a response rate of 16.54%. Most of the respondents were females (85.2%, 253), below 40 years of age (28.3%, 84), employed (63.6%, 189), and in a relationship (56.4%, 167). A historical depression diagnosis (OR = 3.15; 95% CI: 1.39–7.14) was a significant predictor of moderate to severe MDD in our study. The unemployed individuals were two times more likely to report moderate to severe symptoms of MDD than employed individuals (OR = 2.46; 95% CI: 1.06–5.67). Among the total sample population, the moderate to severe MDD prevalence was 50.4%, whereas it was 56.1% among those living in areas affected by wildfires. Conclusion: Based on our study findings, unemployment and a history of depression diagnosis were independently significant risk factors associated with the developing moderate to severe MDD symptoms during wildfire disasters. Further research is required to identify robust predictors of mental health disorders in disaster survivors and provide appropriate interventions to the most vulnerable communities and individuals.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".