The Association Between Korean American Nurse and Primary Care Provider Burnout, Areas of Worklife, and Perceptions of Pandemic Experience: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: Korean immigrants are among the fastest-growing ethnic minority groups and make up the fifth-largest Asian group in the United States. A better understanding of the work environment factors and its impact on Korean American nurse and primary care provider (PCP) burnout may guide the development of targeted strategies to help mitigate burnout and workplace stressors, which is critical for the retention of Korean American nurses and PCPs to promote better alignment of national demographic trends and meet patients' preference for cultural congruence with their health care providers (HCPs). Although there is a growing number of studies on HCP burnout, a limited number of studies specifically focus on the experience of ethnic minority HCPs, particularly during the COVID-19 pandemic. OBJECTIVE: In light of these gaps in literature, the aim of this study was to assess burnout among Korean American HCPs and to identify work conditions during a pandemic that may be associated with Korean American nurse and PCP burnout. METHODS: A total of 184 Korean American HCPs (registered nurses [RNs]: n=97; PCPs: n=87) practicing in Southern California responded to a web-based survey between February and April 2021. The Maslach Burnout Inventory, Areas of Worklife Survey, and Pandemic Experience & Perceptions Survey were used to measure burnout and work environment factors during the pandemic. A multivariate linear regression analysis was used to assess work environment factors associated with the 3 subcategories of burnout. RESULTS: No significant differences were found in the level of burnout experienced by Korean American nurses and PCPs. For RNs, greater workload (P<.001), lower resource availability (P=.04), and higher risk perception (P=.02) were associated with higher emotional exhaustion. Greater workload was also associated with higher depersonalization (P=.003), whereas a greater (professional) community (P=.03) and higher risk perception (P=.006) were associated with higher personal accomplishment. For PCPs, greater workload and poor work-life balance were associated with higher emotional exhaustion (workload: P<.001; worklife: P=.005) and depersonalization (workload: P=.01; worklife: P<.001), whereas only reward was associated with personal accomplishment (P=.006). CONCLUSIONS: Findings from this study underscore the importance of strategies to promote a healthy work environment across multiple levels that recognize demographic variation among Korean American RNs and PCPs, potentially influencing their burnout mitigation needs. A growing recognition of identity-informed burnout experiences across frontline Korean American RNs and PCPs argues for future explorations that capture nuance both across and within this and other ethnic minority nurse and PCP groups. By recognizing and capturing these variations, we may better support the creation of targeted, burnout-mitigating strategies for all.
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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 | 0,002 | 0,000 |
| 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 | 0,003 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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.
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