Gendered socio-economic and mental health effects of the COVID-19 pandemic among adults living in selected informal settlements in Kenya: an intersectional analysis
Notice bibliographique
Résumé
Background COVID-19 pandemic had devastating socio-economic and health effects, including mental health. This study examines the intersectionality between gender and mental health outcomes among Kenyan adults in informal settlements of Nairobi, Kisumu, and Kilifi Counties during the COVID-19 crisis. This is necessary to inform mental health response in case of another pandemic. Methods We analyzed data collected in a longitudinal survey between July 2020 (fourth round) and February 2021 (fifth round). The data covered COVID-19-related effects on job loss, food insecurity, access to health services, and mental health. Participants were randomly sampled from existing cohorts at the Population Council. The outcomes of interest were depressive and anxiety disorders, combined into a binary composite outcome variable. Descriptive statistics included means for continuous variables and frequencies and proportions for categorical variables. Chi-square tests were used to examine the differences between groups. The relationship between the gendered COVID-19 socio-economic effects and participants’ mental health was examined using modified Poisson regression. Results A total of 4,050 participants were interviewed, 66% female and median age 38 [interquartile range (IQR): 29–46]. Complete income loss was strongly associated with negative mental health outcomes in multiple intersections with varied magnitudes. The association was larger among older females (≥50 years) (PR = 1.33, 95% CI = 1.17–1.51, p < 0.001) than older males (PR = 1.22, 95% CI = 1.17–1.27, p < 0.001). Partial loss of income was protective against negative mental health outcomes among young males (18–29 years) (PR = 0.81, 95% CI = 0.76–0.87, p < 0.001) but linked to negative outcomes among middle-aged males (30–49 years old) (PR = 1.14, 95% CI = 1.12–1.16, p < 0.001). Skipping meals was associated with negative mental health outcomes for both genders particularly the married (married male: PR = 1.49, 95% CI = 1.22–1.83, p < 0.001; married female: PR = 1.42, 95% CI = 1.26–1.60, p < 0.001). Conclusion We observed significant gender differences in the prevalence of depressive symptoms and anxiety disorders during the COVID-19 pandemic, underscoring the importance of socio-economic factors and health services access in shaping mental health outcomes. Interventions targeting pandemic-related mental health issues should be gender-sensitive and address economic vulnerabilities such as job losses and food insecurity. Policies to mitigate these effects, especially for at-risk groups are crucial for reducing mental health burden in future crises.
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 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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 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 ».