An Examination of Exposure and Vulnerability to Stress From Chronic Illness and Its Impact on Mental Health and Long-Term Disability Among Non-Hispanic White, African American, and Latinx Populations
Notice bibliographique
Résumé
Abstract Purpose This study examines chronic illness, disability and social inequality within an exposure-vulnerabilities theoretical framework. Methodology/Approach Using the National Survey of Drug Use and Health (NSDUH), a preeminent source of national behavioral health estimates of chronic medical illness, stress and disability, for selected sample years 2005–2014, we construct and analyze two foundational hypotheses underlying the exposure-vulnerabilities model: (1) greater exposure to stressors (i.e., chronic medical illness) among racial/ethnic minority populations yields higher levels of serious psychological distress, which in turn increases the likelihood of medical disability; (2) greater vulnerability among minority populations to stressors such as chronic medical illness exacerbates the impact of these conditions on mental health as well as the impact of mental health on medical disability. Findings Results of our analyses provided mixed support for the vulnerability (moderator) hypothesis, but not for the exposure (mediation) hypothesis. In the exposure models, while Blacks were more likely than Whites to have a long-term disability, the pathway to disability through chronic illness and serious psychological distress did not emerge. Rather, Whites were more likely than Blacks and Latinx to have a chronic illness and to have experienced severe psychological distress (both of which themselves were related to disability). In the vulnerability models, both Blacks and Latinx with chronic medical illness were more likely than Whites to experience serious psychological distress, although Whites with serious psychological distress were more likely than these groups to have a long-term disability. Research Limitations Several possibilities for understanding the failure to uncover an exposure dynamic in the model turn on the potential intersectional effects of age and gender, as well as several other covariates that seem to confound the linkages in the model (e.g., issues of stigma, social support, education). Originality/Value This study (1) extends the racial/ethnic disparities in exposure-vulnerability framework by including factors measuring chronic medical illness and disability which: (2) explicitly test exposure and vulnerability hypotheses in minority populations; (3) develop and test the causal linkages in the hypothesized processes, based on innovations in general structural equation models, and lastly; (4) use national population estimates of these conditions which are rarely, if ever, investigated in this kind of causal framework.
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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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 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 ».