Exploring the Impact of 2023 Wildfires on Generalized Anxiety Disorder Symptoms among Residents in Alberta and Nova Scotia
Bibliographic record
Abstract
Introduction Raging wildfires are rising in diverse areas, leading to significant environmental and psychological repercussions that are drawing growing concern. Objectives This study seeks to assess the prevalence of likely Generalized Anxiety Disorder (GAD) and investigate the factors contributing to its occurrence amidst the wildfires in Alberta and Nova Scotia. Methods Data were collected online through a cross-sectional survey from May 14 to June 23, 2023. Alberta and Nova Scotia participants self-subscribe to the program by texting ‘HopeAB’ or ‘HopeNS’ to a designated short code, respectively. The GAD-7 validated scale assessed likely GAD symptoms among the participants. Results There were 298 respondents in this study, with a majority residing in Alberta/Nova Scotia areas affected by recent wildfires (62.3%). Among the respondents, 41.9% were likely to experience Generalized Anxiety Disorder (GAD) symptoms. Those living in regions recently impacted by wildfires in Alberta/Nova Scotia were found to be twice as likely to have GAD symptoms, with an odds ratio of 2.4 and a confidence interval of 95% ranging from 1.3 to 4.3. Conclusions The study’s findings highlight a relationship between living in areas affected by wildfires and the likelihood of experiencing generalized anxiety disorder (GAD). Exploring potential predictors through additional research could aid in developing strategies to alleviate the mental health impact of natural disasters. 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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".