Post-Traumatic Stress Disorder, Major Depressive Disorder, and Wildfires: A Fifth-Year Postdisaster Evaluation among Residents of Fort McMurray
Notice bibliographique
Résumé
Background: Over 90,000 residents had to be evacuated from Fort McMurray (FMM), Alberta, Canada due to the wildfire that engulfed the city in May 2016. Overall, about 2400 homes or 10% of the housing stock in Fort McMurray were destroyed. The fire consumed about 200,000 hectors of forest, reaching into Saskatchewan. During major disasters, communities’ infrastructure is disrupted, and psychological, economic, and environmental effects are felt for years afterwards. Objective: Five years after the wildfire disaster, this study assessed the prevalence rate of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) in Fort McMurray residents and determined the demographic, clinical, and other risk factors of probable MDD and PTSD. Methodology: A quantitative cross-sectional survey was conducted to collect data through an online questionnaire administered via REDCap between 24 April and 2 June 2021. The Patient Health Questionnaire (PHQ-9) was used to assess the presence of MDD symptoms in respondents. The PTSD Checklist for DSM-5 (PCL-C) was used to assess likely PTSD in respondents. Descriptive, univariate, and multivariate regression analyses were employed. Results: 186 out of 249 individuals who accessed the survey link completed it (74.7% response rate). The median age of the subscribers was 42. The sample included a majority of 159 (85.5%) females; 98 (52.7%) > 40 years of age; 175 (94.1%) employed; and 132 (71%) in a relationship. The overall prevalence of MDD symptoms in our study sample was 45.0% (76). Four variables independently predicted MDD symptoms in the multivariate logistic regression model, including: unemployed (OR = 12.39; 95% CI: 1.21−126.37), have received a mental diagnosis of MDD (OR = 4.50; 95% CI: 1.57−12.92), taking sedative-hypnotics (OR = 5.27; 95% CI: 1.01−27.39), and willingness to receive mental health counseling (OR = 4.90; 95% CI: 1.95−12.31). The prevalence of likely PTSD among our respondents was 39.6% (65). Three independent variables: received a mental health depression diagnosis from a health professional (OR = 4.49; 95% CI: 1.40−14.44), would like to receive mental health counseling (OR = 4.36, 95% CI: 1.54−12.34), and have only limited or no support from family (OR = 11.01, 95% CI: 1.92−63.20) contributed significantly to the model for predicting likely PTSD among respondents while controlling the other factors in the regression model. Conclusions: According to this study, unemployment, taking sleeping pills, having a prior depression diagnosis, and the willingness to receive mental health counseling significantly increase the odds of having MDD and PTSD following wildfires. Family support may protect against the development of these conditions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».