Abstract Integrated vocational education is increasingly essential for addressing maternal health disparities in under-resourced regions of China. Persistent workforce shortages and fragmented training models have contributed to inconsistent care delivery, particularly in Western provinces. This study evaluated an integrated model that combines simulation-based learning, structured supervision, and early clinical immersion to enhance training effectiveness. A convergent parallel mixed-methods design assessed the model’s impact on patient satisfaction, student interaction, and perceived maternal complications. Quantitative data were collected from 300 participants, including students, maternity nurses, clinical instructors, and postnatal patients, and analyzed using SPSS and partial least squares structural equation modeling (SmartPLS). Qualitative insights from 14 semi-structured interviews were analyzed thematically using NVivo. The integrated training model significantly predicted higher patient satisfaction (β = 0.863, R² = 0.743), aligning with Kolb’s Experiential Learning Theory. However, no significant relationships were found between student interaction, patient satisfaction, or perceived maternal complications. Critically, the staff training construct demonstrated weak psychometric validity (CR = 0.337; AVE = 0.126), warranting cautious interpretation of its statistical strength. Qualitative findings provided essential context, revealing fragmented implementation, unclear student roles, and institutional barriers such as emergency delays and staffing shortages. These systemic constraints help explain the absence of improvement in clinical outcomes despite enhanced satisfaction ratings. In conclusion, integrated vocational education can enhance perceived care quality, but its clinical impact depends on institutional readiness and supervisory fidelity. This study offers actionable insights for policy reforms under the Healthy China 2030 agenda and supports scalable models for maternal health training in low-resource settings.
Weitai Luo (Wed,) studied this question.