Impact of the COVID-19 pandemic on the mental health and well-being of Veterans’ spouses: a cross sectional analysis
Notice bibliographique
Résumé
BACKGROUND: COVID-19 has negatively impacted the mental health and well-being of both Canadians and the world as a whole, with Veterans, in particular, showing increased rates of depression, anxiety, and PTSD. Spouses and common-law partners often serve as primary caregivers and sources of support for Veterans, which may have a deleterious effect on mental health and increase risk of burnout. Pandemic related stressors may increase burden and further exacerbate distress; yet the effect of the pandemic on the mental health and well-being of Veterans' spouses is currently unknown. This study explores the self-reported mental health and well-being of a group of spouses of Canadian Armed Forces Veterans and their adoption of new ways to access healthcare remotely (telehealth), using baseline data from an ongoing longitudinal survey. METHODS: Between July 2020 and February 2021, 365 spouses of Veterans completed an online survey regarding their general mental health, lifestyle changes, and experiences relating to the COVID-19 pandemic. Also completed were questions relating to their use of and satisfaction with health-care treatment services during the pandemic. RESULTS: Reported rates of probable major depressive disorder (MDD), generalized anxiety disorder (GAD), alcohol use disorder (AUD), and PTSD were higher than the general public, with 50-61% believing their symptoms either directly related to or were made worse by the pandemic. Those reporting being exposed to COVID-19 were found to have significantly higher absolute scores on mental health measures than those reporting no exposure. Over 56% reported using telehealth during the pandemic, with over 70% stating they would continue its use post-pandemic. CONCLUSIONS: This is the first Canadian study to examine the impact of the COVID-19 pandemic specifically on the mental health and well-being of Veterans' spouses. Subjectively, the pandemic negatively affected the mental health of this group, however, the pre-pandemic rate for mental health issues in this population is unknown. These results have important implications pertaining to future avenues of research and clinical/programme development post-pandemic, particularly relating to the potential need for increased support for spouses of Veterans, both as individuals and in their role as supports for Veterans.
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 | 0,001 | 0,000 |
| 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 | 0,000 | 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 ».