The Association Between Korean American Nurse and Primary Care Provider Burnout, Areas of Worklife, and Perceptions of Pandemic Experience: Cross-sectional Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".