Devastating Wildfires and Mental Health: Major Depressive Disorder Prevalence and Associated Factors among Residents in Alberta and Nova Scotia, Canada
Notice bibliographique
Résumé
Background: Since March 2023, hundreds of fires have burned from coast to coast throughout the country, placing Canada on track to have the worst wildfire season ever recorded. From East to West, provinces such as Quebec, Ontario, Nova Scotia, Alberta, and British Columbia have been particularly affected by large and uncontrollable wildfires. Objectives: The objective of this study was to determine the prevalence of depression symptoms and predictors among residents living in extreme climate conditions during the Canadian wildfires of 2023 in Alberta and Nova Scotia and to update the literature with data related to those wildfires. Methods: A cross-sectional quantitative survey was conducted in this study. REDCap was used to administer an online survey between 14 May and 23 June 2023. Through the Text4Hope program, participants subscribe to receive supportive SMS messages daily. As part of the initial welcome message, participants were invited to complete an online questionnaire, containing demographic information, wildfire-related information, and responses to the Patient Health Questionnaire-9 (PHQ-9) for depression assessment. SPSS version 25 was used to analyze the data. Descriptive, univariate, and multivariate regression analyses were employed. Results: A total of 298 respondents completed the survey out of 1802 who self-subscribed to the Text4Hope program in Alberta and Nova Scotia and received a link to the online survey, producing a response rate of 16.54%. Most of the respondents were females (85.2%, 253), below 40 years of age (28.3%, 84), employed (63.6%, 189), and in a relationship (56.4%, 167). A historical depression diagnosis (OR = 3.15; 95% CI: 1.39–7.14) was a significant predictor of moderate to severe MDD in our study. The unemployed individuals were two times more likely to report moderate to severe symptoms of MDD than employed individuals (OR = 2.46; 95% CI: 1.06–5.67). Among the total sample population, the moderate to severe MDD prevalence was 50.4%, whereas it was 56.1% among those living in areas affected by wildfires. Conclusion: Based on our study findings, unemployment and a history of depression diagnosis were independently significant risk factors associated with the developing moderate to severe MDD symptoms during wildfire disasters. Further research is required to identify robust predictors of mental health disorders in disaster survivors and provide appropriate interventions to the most vulnerable communities and individuals.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».