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BMC Prim Care ; 25(1): 154, 2024 May 06.
Article in English | MEDLINE | ID: mdl-38711072

ABSTRACT

OBJECTIVE: This research aimed to identify the fundamental and geographic characteristics of the primary healthcare personnel mobility in Nanning from 2000 to 2021 and clarify the determinants that affect their transition to non-primary healthcare institutions. METHODS: Through utilizing the Primary Healthcare Personnel Database (PHPD) for 2000-2021, the study conducts descriptive statistical analysis on demographic, economic, and professional aspects of healthcare personnel mobility across healthcare reform phases. Geographic Information Systems (QGIS) were used to map mobility patterns, and R software was employed to calculate spatial autocorrelation (Moran's I). Logistic regression identified factors that influenced the transition to non-primary institutions. RESULTS: Primary healthcare personnel mobility is divided into four phases: initial (2000-2008), turning point (2009-2011), rapid development (2012-2020), and decline (2021). The rapid development stage saw increased mobility with no spatial clustering in inflow and outflow. From 2016 to 2020, primary healthcare worker mobility reached its peak, in which the most significant movement occurred between township health centers and other institutions. Aside from their transition to primary medical institutions, the primary movement of grassroots health personnel predominantly directs towards secondary general hospitals, tertiary general hospitals, and secondary specialized hospitals. Since 2012, the number and mobility distance of primary healthcare workers have become noticeably larger and remained at a higher level from 2016 to 2020. The main migration of primary healthcare personnel occurred in their districts (counties). Key transition factors include gender, education, ethnicity, professional category, general practice registration, and administrative division. CONCLUSIONS: This study provides evidence of the features of primary healthcare personnel mobility in the less developed western regions of China, in which Nanning was taken as a case study. It uncovers the factors that impact the flow of primary healthcare personnel to non-primary healthcare institutions. These findings are helpful to policy refinement and support the retention of primary healthcare workers.


Subject(s)
Primary Health Care , Humans , China , Primary Health Care/statistics & numerical data , Male , Female , Health Personnel/statistics & numerical data , Geographic Information Systems , Career Mobility , Health Workforce/trends , Health Workforce/statistics & numerical data , Health Care Reform
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