Association Between Discrimination and Depressive Symptoms Among Hispanic or Latino Adults During the COVID-19 Pandemic: Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: Discrimination and xenophobia toward Hispanic and Latino communities increased during the COVID-19 pandemic, likely inflicting significant harm on the mental health of Hispanic and Latino individuals. Pandemic-related financial and social instability has disproportionately affected Hispanic and Latino communities, potentially compounding existing disparities and worsening mental health. OBJECTIVE: This study aims to examine the association between discrimination and depressive symptoms during the COVID-19 pandemic among a national sample of Hispanic and Latino adults. METHODS: Data from a 116-item web-based nationally distributed survey from May 2021 to January 2022 were analyzed. The sample (N=1181) was restricted to Hispanic or Latino (Mexican or Mexican American, Puerto Rican; Cuban or Cuban American, Central or South American, and Dominican or another Hispanic or Latino ethnicity) adults. Depression symptoms were assessed using the 2-item Patient Health Questionnaire. Discrimination was assessed using the 5-item Everyday Discrimination Scale. A multinomial logistic regression with a block entry model was used to assess the relationship between discrimination and the likelihood of depressive symptoms, as well as examine how controls and covariates affected the relationship of interest. RESULTS: Mexican or Mexican American adults comprised the largest proportion of the sample (533/1181, 45.13%), followed by Central or South American (204/1181, 17.3%), Puerto Rican (189/1181, 16%), Dominican or another Hispanic or Latino ethnicity (172/1181, 14.6%), and Cuban or Cuban American (83/1181, 7.03%). Approximately 31.26% (367/1181) of the sample had depressive symptoms. Regarding discrimination, 54.56% (634/1181) reported experiencing some form of discrimination. Compared with those who did not experience discrimination, those who experienced discrimination had almost 230% higher odds of depressive symptoms (adjusted odds ratio [AOR] 3.31, 95% CI 2.42-4.54). Also, we observed that sociodemographic factors such as age and gender were significant. Compared with participants aged 56 years and older, participants aged 18-35 years and those aged 36-55 years had increased odds of having depressive symptoms (AOR 3.83, 95% CI 2.13-6.90 and AOR 3.10, 95% CI 1.74-5.51, respectively). Women had higher odds of having depressive symptoms (AOR 1.67, 95% CI 1.23-2.30) than men. Respondents with an annual income of less than US $25,000 (AOR 2.14, 95% CI 1.34-3.41) and US $25,000 to less than US $35,000 (AOR 1.89, 95% CI 1.17-3.06) had higher odds of depressive symptoms than those with an annual income of US $50,000 to less than US $75,000. CONCLUSIONS: Our findings provide significant importance especially when considering the compounding, numerous socioeconomic challenges stemming from the pandemic that disproportionately impact the Hispanic and Latino communities. These challenges include rising xenophobia and tensions against immigrants, inadequate access to mental health resources for Hispanic and Latino individuals, and existing hesitations toward seeking mental health services among this population. Ultimately, these findings can serve as a foundation for promoting health equity.
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 | 0,001 | 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».