Prevalence and changes in boredom, anxiety and well-being among Ghanaians during the COVID-19 pandemic: a population-based study
Notice bibliographique
Résumé
BACKGROUND: The outbreak of the COVID-19 pandemic has been associated with several adverse health outcomes. However, few studies in sub-Saharan Africa have examined its deleterious consequences on mental health. Therefore, we investigated the prevalence and changes in boredom, anxiety and psychological well-being before and during the COVID-19 pandemic in Ghana. METHODS: Data for this study were drawn from an online survey of 811 participants that collected retrospective information on mental health measures including symptoms of generalized anxiety disorder, boredom, and well-being. Additional data were collected on COVID-19 related measures, biosocial (e.g. age and sex) and sociocultural factors (e.g., education, occupation, marital status). Following descriptive and psychometric evaluation of measures used, multiple linear regression was used to assess the relationships between predictor variables and boredom, anxiety and psychological well-being scores during the pandemic. Second, we assessed the effect of anxiety on psychological well-being. Next, we assessed predictors of the changes in boredom, anxiety, and well-being. RESULTS: Before the COVID-19 pandemic, 63.5% reported better well-being, 11.6% symptoms of anxiety, and 29.6% symptoms of boredom. Comparing experiences before and during the pandemic, there was an increase in boredom and anxiety symptomatology, and a decrease in well-being mean scores. The adjusted model shows participants with existing medical conditions had higher scores on boredom (ß = 1.76, p < .001) and anxiety (ß = 1.83, p < .01). In a separate model, anxiety scores before the pandemic (ß = -0.25, p < .01) and having prior medical conditions (ß = -1.53, p < .001) were associated with decreased psychological well-being scores during the pandemic. In the change model, having a prior medical condition was associated with an increasing change in boredom, anxiety, and well-being. Older age was associated with decreasing changes in boredom and well-being scores. CONCLUSIONS: This study is the first in Ghana to provide evidence of the changes in boredom, anxiety and psychological well-being during the COVID-19 pandemic. The findings underscore the need for the inclusion of mental health interventions as part of the current pandemic control protocol and public health preparedness towards infectious disease outbreaks.
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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 | 0,001 | 0,001 |
| 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 | 0,000 |
| 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 ».