Quality of Life Measured with the WHO-5 Wellness Index during Wildfire Season in Two Canadian Provinces—Cross-Sectional Study
Notice bibliographique
Résumé
Introduction: Wildfires impact large populations worldwide with increasing frequency and severity. In Canada, the fire season has affected more areas this year with potential implications for individuals’ well-being and quality of life (QoL). Objective: This study aimed to explore data related to the well-being and QoL of individuals living in areas impacted by wildfires in two Canadian provinces. Methodology: A cross-sectional survey was used to collect data from the residents in the two provinces who subscribed to the Text4Hope mental health support service. Descriptive and inferential statistics were applied using World Health Organization Well-Being Index (WHO-5). Results: Out of 1802 Text4Hope subscribers, 298 responded to the baseline surveys, yielding a response rate of (16.5%). The mean score of QoL was (40.8/100 ± 20.7). Most respondents were from Alberta (84.2%), 40 years old or below (28.3%), females (85.2%), Caucasian (83.5%), in a relationship (56.4%), employed (63.6%), received diagnoses of depression (56.6%), and anxiety (52.9%).The overall prevalence of low QoL was (67.3%; 95% CI: 61.2–73.1%) that was mostly reported among subscribers who were from Nova Scotia (70.5%), 40 years old or younger (71.2%), other gender (83.3%), Black/Hispanic and other ethnicity (85.7% each), having high-school or less education (70.3%), not in a relationship (74.1%), and unemployed (73.6%). In terms of clinical factors, low QoL was most prevalent among those who received the diagnoses of depression (74%) and anxiety (74.3%), and those who have been receiving antidepressants (71.8%) or benzodiazepines (93.3%). Regarding wildfire-related factors, the highest prevalence of low QoL was reported among those living in a region that has recently been impacted by the wildfires (74.7%) and those who have been less frequently watching television images about the devastation caused by the recent wildfires (72.6%). The multivariate logistic regression analysis model predicting the low QoL including the various variables was statistically significant; Χ2 (df = 19; n = 254) = 31.69, p = 0.03. It was found that living in a region impacted by wildfires (37.9%) was the only significant predictor of low QoL (adjusted OR: 1.96; 95% CI: 1.05–3.65). Conclusions: The impact of wildfire on the QoL and well-being among people living in impacted regions is significant. It is empirical for the health authorities to support those who are disadvantaged by wildfire via running of screening programs to early identify mental health symptoms and addressing the living conditions of the survivors, along with the provision of innovative means of mental health support. This necessitates enhanced planning of the governments and health authorities to overcome such adverse psychological consequences of these events.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».