Mental Health Services Following a Natural Disaster Evacuation
Bibliographic record
Abstract
Background On May 4th, 2016 the city of Fort McMurray (FMM), Canada was evacuated as a result of a wildfire. Nearly 89,000 residents were evacuated. The purpose of this study was to examine the use of health services related to mental health. Methods The province of Alberta maintains a publicly funded, universally available health care system. A cohort of residents of FMM was created (n = 88,534) and linked data on visits to physician, emergency departments, as well as pharmaceutical dispensations. Three time periods were examined – pre-evacuation (Jan 1, 2015 to May 3, 2016), evacuation (May 3 to June 1), and post-evacuation (June 1, 2016 to Dec 31, 2016). Results The percentage of the cohort filling prescriptions for anti-anxiety, depression, or sleep aid medication increased significantly the week following the evacuation, before falling to historical levels. Emergency department visits related to mental health problems doubled during the period of evacuation and then fell to historical levels in the post-evacuation period. Hospital separations (discharge, transfer, death) increased the day before the full evacuation notice was given. The number of physician visits increased 107% during the evacuation period and remained elevated at 79% above historical levels. Conclusions The percentage of the population requiring prescription drugs for mental health problems did not change in the post-evacuation period. The increase in prescriptions being filled the week of the evacuation suggests that this was due to replacing medication lost during evacuation. Emergency department visits increased suggesting emergency departments in other areas of the province were being used for primary care. Anxiety levels remained elevated, despite no change in prescription drug dispensations. Key messages: Large natural disasters appear to elevate the risk of being diagnosed with an anxiety disorder, but not depression. Health service use generally decreases to predisaster levels, with the exception of anxiety.
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 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 | 0.022 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".