Mental Health During the COVID-19 Pandemic in the United States: Online Survey
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has had numerous worldwide effects. In the United States, there have been 8.3 million cases and nearly 222,000 deaths as of October 21, 2020. Based on previous studies of mental health during outbreaks, the mental health of the population will be negatively affected in the aftermath of this pandemic. The long-term nature of this pandemic may lead to unforeseen mental health outcomes and/or unexpected relationships between demographic factors and mental health outcomes. OBJECTIVE: This research focused on assessing the mental health status of adults in the United States during the early weeks of an unfolding pandemic. METHODS: Data was collected from English-speaking adults from early April to early June 2020 using an online survey. The final convenience sample included 1083 US residents. The 71-item survey consisted of demographic questions, mental health and well-being measures, a coping mechanisms checklist, and questions about COVID-19-specific concerns. Hierarchical multivariable logistic regression was used to explore associations among demographic variables and mental health outcomes. Hierarchical linear regression was conducted to examine associations among demographic variables, COVID-19-specific concerns, and mental health and well-being outcomes. RESULTS: Approximately 50% (536/1076) of the US sample was aged ≥45 years. Most of the sample was White (1013/1054, 96%), non-Hispanic (985/1058, 93%), and female (884/1073, 82%). Participants reported high rates of depression (295/1034, 29%), anxiety (342/1007, 34%), and stress (773/1058, 73%). Older individuals were less likely to report depressive symptomology (OR 0.78, P<.001) and anxiety symptomology (OR 0.72, P<.001); in addition, they had lower stress scores (-0.15 points, SE 0.01, P<.001) and increased well-being scores (1.86 points, SE 0.22, P<.001). Individuals who were no longer working due to COVID-19 were 2.25 times more likely to report symptoms of depression (P=.02), had a 0.51-point increase in stress (SE 0.17, P=.02), and a 3.9-point decrease in well-being scores (SE 1.49, P=.009) compared to individuals who were working remotely before and after COVID-19. Individuals who had partial or no insurance coverage were 2-3 times more likely to report depressive symptomology compared to individuals with full coverage (P=.02 and P=.01, respectively). Individuals who were on Medicare/Medicaid and individuals with no coverage were 1.97 and 4.48 times more likely to report moderate or severe anxiety, respectively (P=.03 and P=.01, respectively). Financial and food access concerns were significantly and positively related to depression, anxiety, and stress (all P<.05), and significantly negatively related to well-being (both P<.001). Economy, illness, and death concerns were significantly positively related to overall stress scores (all P<.05). CONCLUSIONS: Our findings suggest that many US residents are experiencing high stress, depressive, and anxiety symptomatology, especially those who are underinsured, uninsured, or unemployed. Longitudinal investigation of these variables is recommended. Health practitioners may provide opportunities to allay concerns or offer coping techniques to individuals in need of mental health care. These messages should be shared in person and through practice websites and social media.
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 | 0,007 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».