Quality of life and well-being during the COVID-19 pandemic: associations with loneliness and social isolation in a cross-sectional, online survey of 2,207 community-dwelling older Canadians
Notice bibliographique
Résumé
BACKGROUND: The far-reaching health and social sequelae of the COVID-19 pandemic among older adults have the potential to negatively impact both quality of life (QoL) and well-being, in part because of increased risks of loneliness and social isolation. The aim of this study was to examine predictors of QoL and well-being among Canadian older adults within the context of the pandemic, including loneliness and social isolation. METHODS: This cross-sectional, online survey recruited older adult participants through community organizations and research participant panels. Measures included the: Older People's Quality of Life Scale-B, WHO-5, DeJong Gierveld Loneliness Scale, Lubben Social Network Scale and five COVID-19 specific items assessing impact on loneliness and social isolation. Multiple linear regression models were used to adjust for potential confounders. RESULTS: A total of 2,207 older Canadians (55.7% female, with a mean age of 69.4 years) responded to the survey. Over one-third strongly disagreed that the pandemic had had a significant effect on either their mental (35.0%) or physical health (37.6%). Different patterns of predictors were apparent for QoL and well-being. After adjusting for all variables in the models, the ability of income to meet needs emerged as the strongest predictor of higher QoL, but was not associated with well-being, except for those who chose not to disclose their income adequacy. Age was not associated with either QoL or well-being. Females were more likely to experience lower well-being (β=-2.0, 95% C.I. =-4.0,-0.03), but not QoL. Reporting three or more chronic health conditions and that the COVID-19 pandemic had a negative impact on mental health was associated with lower QoL and well-being. Loneliness was a predictor of reduced QoL (β=-1.4, 95% C.I. =--1.6, -1.2) and poor well-being (β=-3.7, 95% C.I. =-4.3,-3.0). A weak association was noted between QoL and social isolation. CONCLUSIONS: The COVID-19 pandemic is associated with differential effects among older adults. In particular, those with limited financial resources and those with multiple chronic conditions may be at more risk to suffer adverse QoL and well-being consequences. Loneliness may be a modifiable risk factor for decreased QoL and well-being amenable to targeted interventions.
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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 | 0,006 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».