2023 Wildfires in Canada: Living in Wildfire Regions in Alberta and Nova Scotia Doubled the Odds for Residents to Experience Likely Generalized Anxiety Disorder Symptoms
Bibliographic record
Abstract
Background: Wildfires have become increasingly prevalent in various regions, resulting in substantial environmental and psychological consequences that have garnered increasing attention. Objective: This study aims to examine the prevalence of likely Generalized Anxiety Disorder (GAD) and explore the determinants of likely GAD during the wildfires in Alberta and Nova Scotia. Methods: Data were collected online through a cross-sectional survey from 14 May–23 June 2023. Alberta and Nova Scotia participants self-subscribed to the program by texting ‘HopeAB’ or ‘HopeNS’ to a short code, respectively. The GAD-7–validated tool was used to collect information on likely GAD. Results: This study included 298 respondents while one hundred and twelve respondents lived in a region of Alberta/Nova Scotia affected by the wildfires (37.7%). The prevalence of likely GAD among the respondents was 41.9%. Respondents who lived in a region of Alberta/Nova Scotia recently impacted by the wildfires were twice as likely to experience GAD symptoms (OR = 2.4; 95% C.I. 1.3–4.3). Conclusions: The study’s identification of a statistically significant relationship between residing in a wildfire-impacted region and likely GAD shows the association between environmental and psychological well-being. However, the relatively small sample size and self-reported assessment of GAD symptoms may limit the generalizability of the findings. Further research involving a larger sample size delving into potential predictors could facilitate strategies for mitigating the mental health consequences of natural disasters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".