Long-Term Mental Health Effects of a Devastating Wildfire Are Amplified by Socio-Demographic and Clinical Antecedents in Elementary and High School Staff
Bibliographic record
Abstract
OBJECTIVES: To assess the likely prevalence rates of Major Depressive Disorder (MDD), Generalized Anxiety Disorder (GAD) and Post-Traumatic Stress Disorder (PTSD) in staff of Fort McMurray School Districts eighteen months after a May 2016 wildfire, and to determine possible predictors. METHODS: A quantitative cross-sectional survey was used to collect data through self-administered online questionnaires to determine likely MDD, GAD and PTSD using well validated self-report questionnaires. RESULTS: Of 1,446 staff who were sent the online survey link in an e-mail, 197 completed the survey, of which there were 168 females (85%) and 29 males (15%). The one-month prevalence rates for likely MDD, GAD and PTSD among the school staff were 18.3, 15.7 and 10.2% respectively. There were statistically significant associations between multiple socio-demographic and clinical variables likely MDD, GAD and PTSD among respondents. CONCLUSION: Knowledge of key factors for MDD, GAD and PTSD may be helpful for policy makers when formulating population level social and clinical programs, to mitigate the mental health effects of future natural disasters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".