Economic Deterioration and Social Factors Affecting Mental Health During the COVID-19 Pandemic in Japan: Web-Based Cross-Sectional Survey
Notice bibliographique
Résumé
Background: The socioeconomic impact of the COVID-19 pandemic has severely affected individuals' mental health. However, the factors that mitigate or exacerbate the mental health effects of economic deterioration remain underexplored. Objective: This paper analyzes survey data from the second wave of the COVID-19 pandemic in Japan, a period during which women workers were reported to be economically and psychologically vulnerable. The analysis examined factors that mitigate or amplify the impact of COVID-19-induced economic deterioration on mental health, testing 3 hypotheses based on the conservation of resources theory and the stress buffering model: the negative impact of economic deterioration on mental health is greater for individuals with less social support compared to those with more social support (hypothesis 1); the negative impact of economic deterioration on mental health is greater for individuals experiencing more negative interactions compared to those experiencing fewer (hypothesis 2); and the buffering effect of social support is stronger in women than in men, with women receiving less social support experiencing greater mental health impacts from economic deterioration (hypothesis 3). Methods: A web-based survey was conducted by an internet research company in Japan from June to July 2020. A balanced sample of 250 men and 250 women was recruited from each of the following age groups: 20-29, 30-39, 40-49, 50-59, 60-69, and 70-79 years. The analysis focused on working men and women aged 20-50 years (n=1238). Psychological distress was measured using the K6 scale. Economic deterioration was defined as a decrease in income compared to the prepandemic levels, and scales for social support and negative interactions were included. Logistic regression analysis was performed, using K6≥9 as the dependent variable, with interaction terms for each hypothesis sequentially incorporated. Results: In the best-fitting model determined by the Bayesian Information Criterion, a significant association was observed between the interaction of COVID-19-induced economic deterioration and social support with K6 scores (odds ratio [OR] 0.90, 95% CI 0.81-0.99). However, in other models, the interaction between economic deterioration and negative interactions (OR 1.01, 95% CI 0.90-1.13) as well as the 3-way interaction involving economic deterioration, social support, and gender (OR 1.13, 95%CI 0.92-1.39) were not significant. The average marginal effect of economic deterioration was statistically significant for social support scores ranging from 4 to 10. The average marginal effect was 0.11 when social support was 4 (95% CI 0.03-1.20; P=.009) and 0.028 when social support was 10 (95% CI 0.00-0.06; P=.047). Conclusions: The adverse impact of economic deterioration on mental health was more pronounced among individuals with lower levels of social support. These findings support hypothesis 1.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».