What drives them home? Influencing factors of return migration to western China among clinical medicine master's graduates

What drives them home? 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Influencing factors of return migration to western China among clinical medicine master's graduates Qingsong Yang, Zhiqiang Wang, Mengting Zhang, Jing Li, Jinzhong Jia, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9062167/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background To explore the group characteristics, mobility patterns, and key influencing factors associated with the return migration of clinical medicine master's graduates with western China household registration to employment in western China, this study providesevidence for optimizing medical workforce allocation in these areas. Methods Data were derived from the 2024 National Survey on Education Satisfaction among Fresh Medical Postgraduate Graduates. Using convenience sampling, 487 eligible graduates were selected as study subjects. Chi-square tests, t tests, and logistic regression models were employed to systematically analyze employment flow patterns and related influencing factors. Results The return migration group exhibited a city distribution pattern characterized by "stability in third-tier cities and differentiation in second-tier cities," with nearly half (49.57%) choosing employment in third-tier or lower-tier cities. Multivariate logistic regression revealed that family expectations (OR=1.33, 95% CI: 1.01--1.76) served as a significant pull factor for return migration. In contrast, the mentor factor (OR=0.68, 95% CI: 0.55--0.86) and comprehensive geographic‒economic disparity (OR=0.47, 95% CI: 0.27--0.87) constituted major barriers. Graduates with academic degrees were 0.52 times as likely to return as those with professional degrees were (OR=0.52, 95% CI: 0.31--0.88). Graduates with at least one parent in middle- to high-level occupations were 0.51 times as likely to return as those with both parents in grassroots occupations were (OR=0.51, 95% CI: 0.27--0.95). With respect to intercity mobility patterns, consistency between place of origin and employment location emerged as a key factor: when consistent, the odds of graduates choosing downward mobility were 4.46 times greater than those choosing upward mobility (OR=4.46, 95% CI: 2.62--7.58). Conclusions The return employment behavior of clinical medicine master's graduates results from a complex interplay of emotional ties, social capital, and regional economic disparities. Among these factors, the consistency between place of origin and employment location plays a dominant role in the choice of city-tier mobility patterns. Clinical medicine master's graduates Employment in western China Return migration Influencing factors Figures Figure 1 Figure 2 Figure 3 Research Background Employment is fundamental to people's livelihoods, and high-quality full employment serves as a crucial cornerstone of Chinese-style modernization [ 1 ] . Clinical medicine, as a key discipline for safeguarding population health and improving overall societal health levels, has talent cultivation and employment directly linked to the optimal allocation of the health workforce, forming an important foundation for formulating medical education policies and health manpower planning [ 2 ] . Medical postgraduate education spans both education and healthcare—two major areas concerning people's well-being—and constitutes the primary channel for cultivating high-level medical talent in China [ 3 ] . However, China's medical workforce currently faces structural and regional imbalances. According to data from the 2023 China Health Statistical Yearbook, the proportion of licensed physicians with postgraduate degrees remained relatively low in 2022, with postgraduate degree holders accounting for only 19.00% of licensed physicians and 16.70% of licensed (assistant) physicians among health technicians [ 4 ] , indicating an overall shortage of high-level medical talent. A more prominent issue lies in the regional imbalance of health workforce allocation. In western China in particular, the geographic distribution equity of licensed physicians is significantly lower. Although the total number of physicians nationwide has continued to grow, the heterogeneity of spatial distribution has intensified, with western provinces lagging in the allocation of high-quality physician resources, further widening regional disparities in healthcare service levels [ 5 , 6 , 7 , 8 , 9 ] . As the backbone of the future physician workforce, the employment patterns of clinical medicine master's graduates not only concern individual career development but also significantly influence the pattern and quality of the healthcare service system. Existing studies have focused largely on macrolevel dilemmas of "insufficient quantity" and "severe outflow" of health talent in western regions [ 5 , 10 , 11 ] , with less attention given to the microlevel phenomenon of return migration among highly educated medical talent. More importantly, the current literature predominantly emphasizes surveys on graduates' return intentions [ 12 , 13 , 14 ] rather than examining actual return employment behaviors. According to the theory of planned behavior, behavioral intentions cannot fully predict actual behavior [ 15 ] . Investigating actual return employment behaviors can better reveal decision-making logic and influencing factors. Given this gap, the present study focuses on clinical medicine graduates who hold household registrations in western China, who have completed their master's education in nonwestern regions, and who ultimately returned to western China for employment—defining them as the "westward return migration group." This study systematically explores the characteristics of their employment location choices and the influencing factors. The findings will not only contribute to understanding the micromechanisms of return migration among high-level medical talent but also provide scientific evidence for optimizing the regional distribution of medical talent and improving the quality and efficiency of the health workforce structure in western China. Literature Review The Opinions on Implementing the Employment Priority Strategy to Promote High-Quality Full Employment explicitly state the need to "enhance the employment carrying capacity of coordinated regional development and promote balanced interregional employment development" [ 16 ] . Within this policy context, the return migration of college graduates for employment—defined as spatial mobility whereby graduates return from their place of study to their family location or region of origin for job seeking—represents not only geographic return but also significant socioeconomic behavior, encompassing mechanisms such as human capital feedback, social network reconstruction, and regional development feedback [ 17 ] . Examining this phenomenon from the perspective of talent mobility is critically important for understanding the interactive relationship between individual development and regional coordination. When this perspective is focused on the healthcare sector, the issue of talent mobility assumes more distinctive professional attributes and public value. The balanced regional allocation of health human resources serves as the cornerstone for achieving accessible, high-quality healthcare services [ 18 ] . However, China has long faced the structural dilemma of simultaneous "quantitative growth" and "regional imbalance" in its health workforce, with the shortage of high-level clinical physicians in western regions being particularly pronounced—a critical constraint on health equity [ 4 , 5 , 10 , 11 ] . Although some surveys indicate that a considerable proportion of medical master's graduates trained in western institutions secure employment within the region [ 3 , 19 ] , broader research confirms that the "outflow" pressure on western physicians, especially core talent, to eastern coastal areas persists [ 10 ] . The coexistence of "high retention rates" and "high outflow pressure" reveals a deeper issue beyond the static question of "whether they stay": geographic retention does not necessarily equate to occupational stability or willingness for long-term settlement. Therefore, identifying and thoroughly investigating medical graduate groups with potentially greater intrinsic stability may represent the "last mile" in solving the challenge of "retaining" health talent in western China. Theoretically, groups who actively choose return migration may possess the potential to become highly stable cohorts, as their return decisions are often more deeply embedded in noneconomic factors such as local identity, emotional attachment, and family networks—factors that may translate into stronger professional commitment. Simultaneously, their existing local social capital may help reduce adaptation costs and enhance employment stability [ 20 , 21 ] . However, a review of the literature reveals certain limitations in both perspective and depth. First, the study population remains relatively broad, with current discussions largely focused on medical graduates as a whole or on primary health workers [ 2 , 22 , 23 ] . There is a lack of specialized, fine-grained research targeting the core group of "clinical medicine master's graduates"—a cohort characterized by both advanced education and a clear career orientation. Their mobility logic may differ fundamentally from that of undergraduate students or from that of contract-trained graduates. Second, the analytical perspective exhibits partial fragmentation: (a) in factor analysis, most studies list and test individual, family, and institutional factors as objective variables [ 12 , 19 ] , neglecting the subjective perceptions and trade-off processes of graduates as active decision-makers in career choice; (b) in spatial analysis, existing research largely remains at the "regional" level, lacking a perspective that jointly examines "interregional mobility" and more fine-grained "intercity mobility." Given the significant geographic imbalance within the western regions themselves [ 5 ] , overlooking city-tier differences makes it difficult to address the true dilemma of internal imbalances in western talent distribution. These limitations hinder existing research from deeply analyzing the microlevel decision logic of active return migration among highly educated medical professionals, nor can it provide precise guidance for their optimal allocation within western regions. To address these research gaps, this study focuses on the return employment behavior of "clinical medicine master's graduates with western China household registration." Using data from the 2024 National Survey on Education Satisfaction among Fresh Medical Postgraduate Graduates, this study aims to answer the following questions: (1) What pathway patterns emerge from "place of study" to "place of employment" among clinical medicine master's graduates returning to western China for employment? (2) In which tiers of medical institutions is this group concentrated in the western region? What characteristics do their employment quality (initial salary and employment satisfaction) exhibit? (3) What group characteristics define this returning cohort? What are the key factors driving their return behavior? By addressing these questions, this study aims to reveal the micromechanisms underlying the return migration of highly educated medical talent and provide scientific evidence for constructing a talent circulation in western regions that enables them to be "attracted back, retained, and well utilized." Data Source This study utilized data from the National Survey on Education Satisfaction among Fresh Medical Postgraduate Graduates conducted from June to July 2024. The survey employed convenience sampling, with electronic questionnaires distributed online via the Wenjuanxing platform. Prior to implementation, the research team contacted faculty representatives at participating institutions through WeChat groups and 126 email accounts, who then distributed the questionnaire links and QR codes to students. The survey adhered to the principle of voluntary participation, implemented IP address restrictions to prevent duplicate responses, and obtained informed consent from all participants. A total of 105 medical postgraduate training institutions were covered in the survey, including 56 in eastern China, 28 in central China, and 21 in western China. After data cleaning—excluding nonfresh graduates, questionnaires with completion times of less than 360 seconds, and those with missing values—17,476 valid questionnaires were retained for analysis. This study aimed to investigate the factors influencing whether clinical medicine master's students in western China return to western China for employment after graduation. The inclusion criteria were full-time clinical medicine master's students whose family location was in western China and who had completed their studies in nonwestern regions. On the basis of their graduation destinations, the study population was divided into two groups: the "westward return migration group" and the "nonreturn migration to western China group." Applying these criteria, a total of 487 eligible graduates were identified, comprising 355 in the westward return migration group and 132 in the nonreturn migration group. All these genes were included in the final comparative analysis. Variables and measurements Data were collected via a self-designed questionnaire. The employment module of the questionnaire comprises four sections: basic personal information, family characteristics, educational background, and graduation destinations. Specifically, basic personal information included gender and graduates' evaluations of factors influencing career choice. Participants rated the importance of 11 factors influencing their career choice on a five-point Likert scale (1 = not at all important, 5 = extremely important). The full scale is provided as Additional file 1. The family characteristics included household registration type, parental medical background, parental occupation, parental education level, and per capita annual household income. Educational background covered institution type, "double first-class" status, institutional adjustment, and degree type. Parental occupation was classified into three tiers according to the classification method of Yue [ 24 ] : (1) High-level occupations: administrative personnel (officials at the division level or county/township section level and above), senior managers in enterprises or public institutions, medical workers, private entrepreneurs, professional and technical personnel or technical support staff (technicians, etc.); (2) Middle-level occupations: general management and clerical staff, commercial and service personnel; (3) Grassroots-level occupations: self-employed individuals or freelancers, farmers (in agriculture, forestry, animal husbandry, and fishery), workers (production and transportation equipment operators), migrant workers, retirees, unemployed individuals, or other occupations. If both parents were engaged in grassroots-level occupations, this was categorized as "both parents in grassroots occupations"; otherwise, it was categorized as "at least one parent in middle- to high-level occupations." The per capita annual household income was divided into three categories according to the same study [ 24 ] : low-income households (below 8,000 CNY), middle-income households (8,001–29,000 CNY), and high-income households (29,001 CNY and above). The parental education level was classified on the basis of educational attainment: if neither parent had received a university education, the household was defined as having a low education level; otherwise, it was defined as having a high education level. Parental medical background was determined on the basis of occupational experience: if neither parent had experience in medical-related fields, this was considered no medical background; otherwise, it was considered medical background [ 25 ] . To measure regional disparities between graduates' places of study and employment, this study introduces two indicators: geographic distance and economic distance [ 26 ] . Geographic distance was represented by the shortest road distance between the provincial capital cities of the family location and the place of study (based on the shortest driving distance from AutoNavi). Economic distance was measured by the difference in per capita GDP between the provinces of the two locations. In the subsequent logistic regression analysis examining whether graduates returned to western China for employment, an unconditional binary logistic regression model was constructed. To comprehensively reflect the joint influence of "geographic distance" and "economic distance" on return behavior, this study constructed a "comprehensive geographic‒economic disparity" indicator, defined as the economic development level disparity per unit of geographic distance, measuring the comprehensive "spatiotemporal‒economic" barrier graduates must overcome when returning from the province of study to their home province. A larger value of this indicator indicates greater economic disparity per kilometer of geographic distance. To mitigate the impact of extreme values on the model and improve the skewed distribution of the variable, this indicator was logarithmically transformed after shifting. The specific calculation formula was as follows: comprehensive geographic‒economic disparity = log[(economic distance/geographic distance) + 80.65]. In the analysis of employment mobility patterns after returning to western China, drawing on existing studies on medical graduates choosing employment in their places of origin [ 27 , 28 ] , the variable "whether the city of employment is consistent with the place of origin" was included in the multinomial logistic regression model to measure the influence of place of origin on clinical medicine graduates' employment location choices. Graduation destinations included graduates' employment locations, employing institutions, and employment quality. This study adopted the city tier classification released by the New First-Tier Cities Research Institute of China Business Network (CBN) in the *2024 New First-Tier Cities Attractiveness Ranking* [ 29 ] . Building on this and drawing on existing research practices [ 30 ] , this study combined first-tier and new first-tier cities as "first-tier cities," and combined third-tier, fourth-tier, and fifth-tier cities as "third-tier and lower cities." Accordingly, city tiers were ultimately classified into three categories: first-tier, second-tier, and third-tier and below. In addition, per capita GDP data for each province were sourced from the officially released 2024 Statistical Bulletin on National Economic and Social Development of the respective provinces (autonomous regions and municipalities directly under the central government). The detailed variable assignments are presented in Table 1 . Table 1 Description of explanatory variables and coding schemes Actor Specific variable Variable description and reference group Individual characteristics Gender Female (reference) and male Evaluation of factors influencing career choice Availability of permanent staffing, work location, institutional administrative level, position type, salary and benefits, professional relevance, marital status and partner, personal development, family expectations, institutional factors, mentor factors Family characteristics Household registration type Agricultural household registration (reference) and nonagricultural household registration Parental medical background Neither parent has medical background (reference) and at least one parent has medical background Parental occupation Both parents in grassroots occupations (reference) and at least one parent in mid-to-high-level occupations Parental education level Both parents with low education level (reference) and at least one parent with high education level Per capita annual household income Low income (reference), middle income, and high income Educational characteristics Institution type Independent medical colleges (reference) and comprehensive universities "Double First-Class" status No (reference) and Yes Institutional adjustment No (reference) and Yes Degree type Professional degree (reference) and academic degree Regional characteristics Economic distance Difference in per capita GDP between the province of the institution and the province of the family location Geographic distance Shortest road distance between the provincial capital cities of the institution and the family location (based on the shortest driving distance from AutoNavi) Comprehensive geographic-economic disparity Log[(economic distance/geographic distance) + 80.65] Statistical analysis Data analysis was performed via Excel 2010 and SPSS 26.0. Continuous data are presented as the means and standard deviations, whereas categorical data are expressed as frequencies and percentages. Chi-square tests and independent samples t tests were initially conducted to examine differences in the distribution of return migration among clinical medicine master's graduates with different characteristics. On this basis, an unconditional binary logistic regression model was employed to identify key factors influencing their return migration to western China for employment (α = 0.05). Furthermore, for graduates who had returned to western China for employment, multinomial logistic regression was used to explore the factors influencing their city-tier mobility patterns (α = 0.05). Results 1. Migration patterns of clinical medicine master's degree graduates with Western China household registration from the place of study to the place of employment (1) Characteristics of employment city distribution among clinical medicine master's graduates with Western China household registration As shown in Table 2 , among the return migrants employed in western China, nearly half (49.57%) of the graduates flowed to third-tier and lower-tier cities, constituting the most common type of employment city choice, followed by first-tier cities, accounting for 40.85%. In contrast, the nonreturn migration group was highly concentrated in first-tier cities, with a proportion reaching 50.00%, whereas the employment proportions in second-tier cities and third-tier and lower-tier cities decreased successively, at 31.82% and 18.18%, respectively. Table 2 Distribution of employment city tiers and flow patterns among clinical medicine master's graduates with Western China household registration Category (N) Sample size (%) Distribution of employment city tier Return migration group (355) 145 (40.85) First-tier cities 34 (9.58) Second-tier cities 176 (49.57) Third-tier and lower-tier cities Nonreturn migration group (132) 66 (50.00) First-tier cities 42 (31.82) Second-tier cities 24 (18.18) Third-tier and lower-tier cities (2) Characteristics of intercity mobility patterns among clinical medicine master's graduates with Western China household registration As shown in Fig. 1 , the urban mobility patterns of the return-migration group exhibited an overall characteristic of "stability in third-tier cities and differentiation in second-tier cities." Specifically, parallel mobility (referring to cases where the city tier of the place of study was the same as that of the place of employment) in third-tier and lower-tier cities had the highest proportion, reaching 11.27%. Among clinical medicine master's graduates trained in second-tier cities in nonwestern regions, employment location choices displayed a "polarized" pattern: 29.58% flowed to first-tier cities in western China, whereas 29.30% flowed to third-tier and lower-tier cities in western China. In contrast, the urban mobility patterns of the nonreturn migration group were predominantly characterized by "stability in first-tier cities and upgrading from second-tier cities." Among this group, parallel mobility in first-tier cities constituted the highest proportion, at 24.24%. Graduates from second-tier cities in nonwestern regions were more inclined to seek employment in first-tier cities in nonwestern regions, demonstrating a clear tendency toward upward mobility. 2. Employment institutions and employment quality of clinical medicine master's graduates with Western China household registration (1) Characteristics of the employment institution tier distribution among clinical medicine master's graduates with Western China household registration As shown in Fig. 2 , hospitals were the primary employment destinations for clinical medicine master's graduates (including both return migration and nonreturn migration groups). Further analysis of the 411 graduates who chose hospitals (see Table 3 for details) revealed that their employment institution tiers were highly concentrated in tertiary Grade A hospitals, a characteristic consistently observed in both the return migration and nonreturn migration groups. Table 3 Distribution of employment hospital tiers among graduates of clinical medicine master's degrees Hospital tier Return migratione (%) Nonreturn migratione (%) Tertiary Grade A 314(91.55) 97(78.86) Tertiary Grade B 16(4.66) 22(17.89) Tertiary Grade C 3(0.87) 1(0.81) Secondary Grade A 5(1.46) 2(1.63) Nongraded hospitals 5(1.46) 1(0.81) (2) Differences in employment quality among clinical medicine master's graduates with Western China household registration As shown in Fig. 3 , regarding initial salary, the distribution exhibited significant differences between return migrants and nonreturn migrants. A greater proportion of return migrants were in the 3,000–5,000 CNY range, whereas the two groups were roughly comparable in the 5,001–10,000 CNY range. However, in the high-end range of 10,001 CNY and above, the proportion of nonreturn migrants was significantly greater than that of return migrants. This difference in initial salary structure was directly reflected in employment satisfaction, with nonreturn migrants perceiving significantly stronger satisfaction with salary and benefits (see Table 4 for details), a finding that stands in stark contrast to the advantage of this group in the high initial salary segment shown in Fig. 3 . clinical medicine master's graduates by employment flow pattern Table 4 Differences in employment satisfaction among clinical medicine master's graduates by employment flow pattern Category Return migration (Mean ± SD) Nonreturn migration (Mean ± SD) t value P value Employment location 3.97 ± 0.77 4.06 ± 0.78 1.09 0.28 Employing institution 3.98 ± 0.74 4.04 ± 0.77 0.80 0.43 Job position 3.99 ± 0.73 4.07 ± 0.76 1.01 0.31 Salary and benefits 3.70 ± 0.81 3.92 ± 0.80 2.70 < 0.05 Social status 3.90 ± 0.70 4.00 ± 0.78 1.33 0.18 Personal development space 3.87 ± 0.70 3.97 ± 0.79 1.30 0.19 Job stability 3.91 ± 0.70 3.98 ± 0.79 0.91 0.36 Career prospects 3.88 ± 0.71 3.98 ± 0.79 1.28 0.20 3. Factors influencing return to western China among clinical medicine master's graduates with Western household registration (1) Descriptive analysis of factors influencing return to western China among clinical medicine master's graduates with Western household registration Graduates whose parents both had no medical background, both were engaged in grassroots occupations, and held professional degrees presented higher proportions of return migration than those whose parents had at least one medical background or were in middle- to high-level occupations, as well as those with academic degrees ( P < 0.05). In addition, graduates who placed greater importance on work location and family expectations were more likely to return to western China for employment ( P < 0.05). The smaller the comprehensive geographic‒economic disparity is, the stronger the tendency for graduates to return to western China for employment ( P < 0.01), whereas graduates who place greater emphasis on mentor factors are more inclined toward nonreturn migration ( P < 0.05). With respect to other factors, male graduates, those with agricultural household registration, those with parents with lower education levels, those with lower per capita annual household income, those graduating from independent medical colleges or non"double first-class" universities, those who had experienced institutional adjustment, and those who placed greater importance on factors such as the availability of permanent staffing, institutional and position type, salary and benefits, professional relevance, and personal development presented a greater tendency toward return migration to western China. Conversely, those who placed relatively less importance on institutional factors were more likely to choose nonreturn migration. However, none of these differences reached statistical significance. The detailed results are presented in Table 5 . Table 5 Return migration status among clinical medicine master's degrees with different characteristics Variable N Return migration group [n(%)/Mean ± SD] Nonreturn migration group [n(%)/Mean ± SD] χ2/t value P value Gender 0.03 0.86 Female 329 239(72.64) 90(27.36) Male 158 116(73.42) 42(26.58) Household registration type 2.96 0.09 Agricultural 278 211(75.90) 67(24.10) Nonagricultural 209 144(68.90) 65(31.10) Parental medical background 4.23 0.04 Neither parent has medical background 436 324(74.31) 112(25.69) At least one parent has medical background 51 31(60.78) 20(39.22) Parental occupation 10.16 < 0.01 Both parents in grassroots occupations 367 281(76.57) 86(23.43) At least one parent in mid-to-high-level occupations 120 74(61.67) 46(38.33) Parental education level 3.48 0.06 Both parents with low education level 392 293(74.74) 99(25.26) At least one parent with high education level 95 62(65.26) 33(34.74) Per capita annual household income 0.00 1.00 Low income 178 130(73.03) 48(26.97) Middle income 184 134(72.83) 50(27.17) High income 125 91(72.80) 34(27.20) Institution type 0.05 0.83 Independent medical college 299 219(73.24) 80(26.76) Comprehensive university 188 136(72.34) 52(27.66) "Double First-Class" status 0.97 1.00 No 305 227(74.43) 78(25.57) Yes 182 128(70.33) 54(29.67) Institutional adjustment 2.94 0.09 No 283 198(69.96) 85(30.04) Yes 204 157(76.96) 47(23.04) Degree type 7.04 < 0.01 Professional degree 389 294(75.58) 95(24.42) Academic degree 98 61(62.24) 37(37.76) Evaluation of factors influencing career choice Availability of permanent staffing 3.61 ± 1.07 3.42 ± 1.18 -1.71 0.09 Work location 4.19 ± 0.77 4.00 ± 0.91 -2.29 0.02 Institutional nature 4.06 ± 0.75 3.98 ± 0.93 -1.00 0.32 Position type 4.14 ± 0.73 4.02 ± 0.86 -1.44 0.15 Salary and benefits 4.13 ± 0.78 4.11 ± 0.80 -0.26 0.80 Professional relevance 4.11 ± 0.80 4.08 ± 0.86 -0.38 0.71 Marital status and partner 2.92 ± 1.33 3.05 ± 1.32 0.97 0.33 Personal development 4.10 ± 0.78 3.95 ± 1.00 -1.71 0.09 Family expectations 3.68 ± 0.92 3.42 ± 1.17 -2.59 0.01 Institutional factors 3.25 ± 1.12 3.27 ± 1.13 0.18 0.86 Mentor factors 2.95 ± 1.12 3.18 ± 1.20 2.02 0.04 Geographic distance 1570.83 ± 687.63 1661.05 ± 781.10 1.24 0.22 Economic distance 33444.17 ± 30458.11 48469.10 ± 42153.72 3.75 < 0.01 Comprehensive geographic-economic disparity 10.32 ± 34.99 28.74 ± 44.24 4.31 < 0.01 (2) Unconditional binary logistic regression analysis of factors influencing return migration to western China among clinical medicine master's graduates with Western household registration First, whether clinical medicine master's degree graduates returned to Western China for employment was used as the binary dependent variable (0 = no, 1 = yes). All the aforementioned variables were subsequently included in the regression model, and univariate analysis was conducted via the "Enter" method. Given that a strict significance level in univariate analysis might lead to the exclusion of some statistically meaningful variables, the significance threshold for univariate analysis was relaxed to P < 0.1 in this study, meaning that variables with P < 0.1 were considered to have a statistical association with the outcome variable. Finally, variables selected from the univariate analysis that were statistically significant ( P < 0.1) were included in the model for multivariate analysis. The regression analysis results indicated that, from the perspective of individual characteristics, family expectations, mentor factors, and degree type significantly influenced the return migration of clinical medicine master's graduates with western household registration to western China. For each unit increase in the importance of graduates attached to family expectations, the odds of returning to western China for employment increased by a factor of 1.33 (OR = 1.33, 95% CI: 1.01–1.76). In contrast, for each unit increase in the importance attached to mentor factors, the odds of return migration decreased to 0.68 times the original value (OR = 0.68, 95% CI: 0.55–0.86). Furthermore, compared with graduates with professional degrees, those with academic degrees had odds of return migration that were only 0.52 times greater (OR = 0.52, 95% CI: 0.31–0.88). At the family background level, graduates with at least one parent engaged in middle- to high-level occupations had odds of return migration that were only 0.51 times greater than those of graduates with both parents in grassroots occupations (OR = 0.51, 95% CI: 0.27–0.95). From the perspective of regional characteristics, for each unit increase in the comprehensive geographic‒economic disparity, the odds of graduates returning to western China for employment decreased to 0.47 times the original value (OR = 0.47, 95% CI: 0.27–0.82). The detailed results are presented in Table 6 . Table 6 Unconditional binary logistic regression results for factors influencing return migration to western China among clinical medicine master's graduates Variable Comparison Reference β OR 95%CI P value Household registration type Nonagricultural Agricultural -0.16 0.86 0.52 ~ 1.41 0.54 Parental medical background At least one parent has medical background Neither parent has medical background -0.35 0.71 0.35 ~ 1.43 0.33 Parental occupation At least one parent in mid-to-high-level occupations Both parents in grassroots occupations -0.67 0.51 0.27 ~ 0.95 0.03 Parental education level At least one parent with high education level Both parents with low education level 0.21 1.24 0.63 ~ 2.45 0.54 Institutional adjustment Yes No 0.38 1.46 0.92 ~ 2.31 0.11 Degree type Academic degree Professional degree -0.65 0.52 0.31 ~ 0.88 < 0.01 Availability of permanent staffing 0.04 1.04 0.83 ~ 1.30 0.74 Work location 0.22 1.24 0.92 ~ 1.69 0.16 Personal development 0.04 1.04 0.76 ~ 1.43 0.80 Family expectations 0.29 1.33 1.01 ~ 1.76 0.04 Mentor factors -0.38 0.68 0.55 ~ 0.86 < 0.01 Comprehensive geographic-economic disparity -0.75 0.47 0.27 ~ 0.82 < 0.01 Constant 4.35 77.72 < 0.01 (3) Multinomial logistic regression analysis of factors influencing intercity employment patterns among graduates of clinical medicine master's programs with Western household registration returning to western China Using intercity employment mobility patterns as the dependent variable, a multinomial logistic regression model based on the forward:LR method was employed to explore the factors influencing the intercity employment mobility patterns of clinical medicine master's graduates who returned to western China for employment. The factors associated with different employment mobility patterns included "double first-class" status, whether the employment city was the same as the place of origin, and institutional factors in the evaluation of career choice influences ( P < 0.05). The results revealed that graduation from a "double first-class" university, whether the employment city was the same as the place of origin, and the evaluation of institutional factors in career choice ( P < 0.05) were significantly associated with employment mobility patterns. Graduates from "Double First-Class" institutions were 2.89 times more likely to choose parallel mobility (OR = 2.89, 95% CI: 1.56–5.36) and 2.91 times more likely to choose downward mobility (OR = 2.91, 95% CI: 1.65–5.13) than to choose upward mobility. In contrast, graduates who prioritized institutional factors were more inclined to choose upward mobility (OR = 0.59, 95% CI: 0.45–0.77; OR = 0.74, 95% CI: 0.58–0.95). Furthermore, among clinical medicine master's graduates whose employment city was consistent with their place of origin, the likelihood of choosing downward mobility was 4.46 times greater than that of choosing upward mobility (OR = 4.46, 95% CI: 2.62–7.58). The detailed results are presented in Table 7 . Table 7 Multinomial logistic regression analysis of factors influencing intercity employment mobility pattern choices among clinical medicine master's graduates returning to western China Variable Reference Comparison Parallel mobility Downward mobility β OR(95%CI) β OR(95%CI) "Double First-Class" status No Yes 1.06** 2.89(1.56 ~ 5.36) 1.07** 2.91(1.65 ~ 5.13) Employment city consistent with place of origin No Yes 0.31 1.37(0.74 ~ 2.51) 1.50** 4.46(2.62 ~ 7.58) Institutional factors -0.53** 0.59(0.45 ~ 0.77) -0.30* 0.74(0.58 ~ 0.95) Constant 0.87 0.10 *Note: * P < 0.05, * P < 0.01 Conclusions and Discussion On the basis of data from the 2024 National Survey on Education Satisfaction among Fresh Medical Postgraduate Graduates, this study systematically analyzed the return employment behavior of clinical medicine master's graduates with Western China household registration. The main findings are as follows: (1) Conclusions First, clinical medicine master's graduates returning to western China exhibited a "dumbbell-shaped" urban distribution pattern, with consistency between the place of origin and employment city serving as the core driver of downward mobility. This study revealed that the distribution of employment city tiers among this group displayed pronounced bipolar characteristics: first-tier cities (48.85%) and third-tier and lower-tier cities (49.57%) accounted for the highest proportions, whereas second-tier cities (9.58%) were notably underrepresented, forming a "dumbbell-shaped" structure. This stands in stark contrast to the "pyramid-shaped" distribution observed in the nonreturn migration group, which decreased progressively from higher to lower tiers (first-tier 50.00% > second-tier 31.82% > third-tier 18.18%). Contrary to findings from existing studies [ 26 , 30 ] , the proportion of returning graduates who flow to third-tier and lower-tier cities in this study was relatively high. Further analysis of mobility directions revealed that graduates from "Double First-Class" universities were more inclined toward parallel or downward mobility, whereas graduates who placed greater emphasis on institutional factors were more likely to choose upward mobility. This phenomenon is related to the reality that western China has a limited number of first- and second-tier cities and a relatively concentrated urban system structure. According to statistics, first- and second-tier cities in western China account for only 2.08% of all prefecture-level cities in China and are predominantly provincial capitals [ 29 ] . This regional structural constraint objectively limits graduates' employment options in developed cities within western China, leading those with higher expectations of institutional brand value to be more inclined toward first-tier cities to maximize returns on their educational investment. Graduates whose employment city was consistent with their place of origin were 4.46 times more likely to choose downward mobility than upward mobility, highlighting the critical role of place identity in return migration decisions. Scannell and Gifford noted that individuals' emotional attachment to their places of growth influences their spatial behavior decisions through social bonds and cultural identity mechanisms [ 31 ] . The high proportion of employment in third-tier and lower-tier cities reflects the driving effect of local embeddedness: the stock of social capital provided by places of origin (family networks, peer relationships, etc.) can effectively reduce risk and uncertainty in the early career stages, whereas graduates' place identity also makes them more willing to return to familiar SMEs for development. Second, comprehensive geographic‒economic disparity significantly inhibits return migration, with regional development gaps constituting a key barrier to talent return migration. The greater the "economic disparity per unit of geographic distance" between the place of study and the place of origin is, the lower the likelihood of graduates returning. The descriptive statistics indicate that the mean economic disparity in the return migration group (33,444.17 CNY) was significantly lower than that in the nonreturn migration group (48,469.10 CNY, t = 3.75, P < 0.01), indicating that regional economic differences are an important factor influencing talent returns. Domestic research has also confirmed that the greater the economic disparity between the place of study and the place of origin is, the lower the probability of graduates returning [ 26 ] . With respect to employment institution type, clinical medicine master's graduates were highly concentrated in tertiary Grade A hospitals, regardless of whether they returned to Western China. In terms of salary and satisfaction, returnees in western China generally had lower high-end salary levels and lower levels of satisfaction with salary and benefits than nonreturnees did. Third, family factors, educational factors, and mentor factors exerted differential effects on return migration decisions. Family expectations and parents engaged in grassroots occupations had significantly positive effects on return migration, whereas mentor factors had significantly negative effects, revealing the push‒pull dynamics that graduates face between their "family-of-origin networks" and their "academic mentorship networks." Different networks impose competing constraints on individual behavior, with students who are more dependent on their mentors being more inclined to develop in their mentors' locations. Furthermore, graduates with professional degrees showed significantly greater return tendencies than those with academic degrees did, reflecting fundamental differences in the training objectives of the two degree types: professional degrees focus on clinical practice competence, with professional activities strongly dependent on the local healthcare environment; academic degrees emphasize scientific research innovation competence, typically requiring reliance on high-level research platforms, where the concentration of high-quality resources in economically developed regions creates stronger regional attractiveness. (2) Discussion On the basis of the above findings, this study proposes the following policy recommendations at the micro, meso, and macro levels: First, at the micro level, local governments and healthcare institutions can establish targeted talent attraction mechanisms to effectively activate "place identity." International studies have shown that place identity is a key factor influencing healthcare workers' retention intentions, with retention rates being significantly higher among healthcare workers whose place of origin is consistent with their employment location [ 32 ] . Therefore, consistency between place of origin and employment location can be used as an important predictor of stability and retention. A database of medical talent with local household registration should be established, enabling early engagement with local medical students studying elsewhere. Through emotional connections and a sense of belonging, such engagement can guide their return migration and enhance the likelihood of their long-term service in the local area. Second, at the meso level, universities should optimize medical talent training models to increase students' "local embeddedness." Given the higher return tendency observed among graduates with professional degrees, it is recommended that local-oriented components be integrated into the training process. For example, some clinical rotations could be arranged at collaborating hospitals in western regions. Additionally, a dual mentorship system comprising "academic mentors + local career mentors" could be explored, providing simultaneous academic support and local career network connections for students interested in returning. This would help them establish themselves in local areas while maintaining academic linkages. Third, at the macro level, the government should promote coordinated regional development, narrow regional economic disparities, and reduce the "opportunity cost" of talent return migration. Drawing on the experience of Australia's "Rural Medical Incentive Program," combining salary subsidies with career development support has been shown to significantly improve physician retention rates in remote areas [ 33 ] . Given the significant inhibitory effect of comprehensive geographic‒economic disparity on return migration, it is recommended that special position subsidies be provided for master's-level talent working in third-tier and lower-tier cities. Through salary compensation mechanisms, their overall income should be maintained at no less than a certain proportion of comparable positions in eastern regions, thereby enhancing western regions' attractiveness to high-level medical talent. (3) Limitations and future directions This study has several limitations that warrant further improvement in future research. First, with respect to the sampling method, this study employed convenience sampling. Although this approach offers certain advantages in terms of sample coverage, it may introduce bias in accurately reflecting the characteristics of the entire population of clinical medicine master's graduates returning to western China for employment. The proportion of graduates from some western provinces, such as Tibet and Qinghai, was relatively low in the sample, which may inadequately capture the return migration characteristics of medical talent from these regions. Moreover, the western provinces exhibit significant heterogeneity in economic development levels, ethnic composition, and healthcare resources. The imbalanced sample structure may compromise the representativeness and external validity of the study findings. Future research could adopt stratified sampling or quota sampling to ensure better structural balance across dimensions such as province, institution, and city tiers, thereby enhancing the generalizability of the conclusions. Second, regarding variable measurement, the conceptual breadth of certain key variables, without further subdivision, may have compromised the precision of the analysis. For example, "mentor factors," identified as important variables influencing return migration decisions, were measured only by the importance graduates attached to them, without distinguishing subdimensions such as mentors' supervisory style, resource networks, or geographic origins. Similarly, "family expectations" were not further disaggregated into different dimensions, such as "emotional support" versus "economic dependence." Furthermore, this study analyzed clinical medicine as an aggregate whole without further distinguishing its secondary disciplinary directions. In reality, different specialties—such as internal medicine, surgery, obstetrics and gynecology, and pediatrics—may exhibit variations in job market supply and demand, policy support intensity, and individual geographic preferences. Future research could adopt more refined measurements of key variables and conduct in-depth comparisons at the specialty level to increase analytical precision and explanatory power. Third, regarding the research design, this study was based on cross-sectional survey data, which can describe employment status and related factors at a single point in time but cannot reveal the dynamic process of returnees' career development or long-term stability. Cross-sectional data cannot capture postreturn career adaptation, intentions for remigration, or actual mobility behaviors, nor can they determine whether "returns" truly equate to "rootedness." Subsequent research could adopt longitudinal designs and collect data at multiple time points to explore the mechanisms sustaining medical talent return to western China thoroughly, career progression pathways, and remigration trends. Additionally, future studies could extend the research population to related health professions, such as nursing and public health, and conduct interdisciplinary comparative research to develop more universally applicable strategies for attracting health professionals to western regions. Furthermore, mixed-methods approaches could be considered, incorporating qualitative interviews to gain deeper insights into the subjective motivations underlying return decisions, thereby complementing the limitations of quantitative data. Declarations Ethics approval and consent to participate: This study strictly adhered to the ethical guidelines of the Declaration of Helsinki and the principles of data confidentiality. This study was approved by the Ethics Committee of Peking University under the ethical approval number IRB00001052–20074. All participants provided informed consent, and the research was conducted in full compliance with established ethical guidelines and data confidentiality principles. Clinical trial number Not applicable Consent for publication: Not applicable. Competing interests: All the authors declare that they have no competing interests to report. Funding: This research received no external funding. Author Contribution QS Y: Responsible for data processing and analysis, as well as paper writing; MT Z, ZQ W, JL,ZS L, Y J: Participated in paper revision; JZ J: Provided guidance on research design and paper revision. Acknowledgments: We would like to express our sincere gratitude to all the members of the research team for their valuable suggestions and continuous support throughout the paper writing process. Data Availability The datasets generated and/or analyzed during the current study are not publicly available [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author upon reasonable request. References LAI DS. Advancing Chinese-style modernization through high-quality full employment. Chin J Popul Sci. 2022(06):20–5. HOU JL, WANG W M, KE Y. Study on the Graduating Medical Undergraduate Students' Career Preference and Decision. Chin Health Serv Manage. 2018;35(10):765–8. DENG C T, ZHANG X L, CHEN K. Correlation between graduate student source information and destinations in a medical university. Chin J Med Educ Res. 2025;24(07):915–20. National Health Commission of the PRC. 2023 China Health Statistical Yearbook [EB/OL]. (2025-01-24)[2026-03- 07].https://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/202501/8193a8edda0f49df80eb5a8ef5e2547c.shtml ZHAO RN, WANG J, HAN CX. Study on Equity and Development Forecast of Health Human Resources Allocation in the Western of China. Health Econ Res. 2023;40(06):22–5. NADIDA A X M, YIN Y, WU X F, et al. Research on Equity and Demand Prediction of Health Human Resources Allocation in Chinese Hospitals of Traditional Chinese Medicine in 14th Five-year Plan Period. Chin Hosp Manage. 2024;44(04):78–82. Wang Z, He H, Liu X, Deng S, Li Q, Zhang Y, et al. Health resource allocation in Western China from 2014 to 2018. Arch Public Health. 2023;81(1):30. Zhu B, Hsieh C, Mao Y. Spatiot-temporal variations of licensed doctor distribution in China: measuring and mapping disparities. BMC Health Serv Res. 2020;20(1):159. LI Q F, HE Y H, YAN Y L, et al. Study on the Allocation of Health Human Resources in China from 2017 to 2021 Based on Structural Change Degree and Rank Sum Ratio Method. Med Soc. 2024;37(04):55–60. Wang X, Sun Q, Chen L, Li Y, Zhang H, Liu J, et al. Physician turnover in China, 2011–2021: a nationwide longitudinal study. Hum Resour Health. 2025;23(1):40. SONG G, ZHAO Y, MIN T, et al. 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The Family Background and the Doctor Graduate's Employment City Choice. Soc Sci Beijing. 2024(10):105–15. ZHANG Y X, CUI C, LAO X, et al. Impact factors of university graduates’ return migration from perspective of multidimensional distance. Geogr Sci. 2025;45(04):860–71. LI X Y, ZHANG Y, SUN Y X, et al. A study on the job preferences and heterogeneity of medical students from different birthplaces:A discrete choice experiment based on six hospitals in Beijing. Chin J Health Policy. 2024;17(01):51–9. LIU S M, CHEN, Y Y. Analysis on Job Preference of PhD Students in Public Health Related Majors in a University in Shanghai. Med Soc. 2022;35(04):7–11. New First-Tier Cities Research Institute. 2024 New First-Tier Cities Attractiveness Ranking [EB/OL]. (2024-05-30) [2026-03-07]. https://www.yicai.com/news/102130290.html LAO X, ZHANG Y X CUIC, UniversityGraduates’. Intercity Migration Patterns and Its Influencing Factors. Heilongjiang High Educ Res. 2023;41(06):125–33. Scannell L, Gifford R. Defining place attachment: a tripartite organizing framework. J Environ Psychol. 2010;30(1):1–10. Mbemba GIC, Gagnon M, Hamelin-Brabant L. Factors influencing recruitment and retention of healthcare workers in rural and remote areas in developed and developing countries: an overview. J Public Health Afr. 2016;7(2):565. Campbell N, McAllister L, Eley D. The influence of motivation in recruitment and retention of rural and remote allied health professionals: a literature review. Rural Remote Health. 2012;12:1900. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1Likertscale.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 16 Mar, 2026 Submission checks completed at journal 15 Mar, 2026 First submitted to journal 15 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9062167","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623856481,"identity":"5473ead1-70b4-4c07-8c27-a0ca1ba0612f","order_by":0,"name":"Qingsong Yang","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Qingsong","middleName":"","lastName":"Yang","suffix":""},{"id":623856483,"identity":"60d32400-36b0-46f4-8a36-a4031b47f8e2","order_by":1,"name":"Zhiqiang Wang","email":"","orcid":"","institution":"Shihezi 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University","correspondingAuthor":false,"prefix":"","firstName":"Zhisheng","middleName":"","lastName":"Liang","suffix":""},{"id":623856492,"identity":"14d7c01e-7a9a-4282-91e2-cd3da8797c10","order_by":6,"name":"Yu Jin","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2026-03-08 05:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9062167/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9062167/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107101789,"identity":"b99680fc-4142-4712-bb62-a7c57427df75","added_by":"auto","created_at":"2026-04-16 19:16:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118858,"visible":true,"origin":"","legend":"\u003cp\u003ePatterns of changes in city tiers from the place of study to the place of employment among graduates returning to western China\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9062167/v1/b9cf69763896555d1f5e2370.jpg"},{"id":107483008,"identity":"d2c3d971-ca6d-44b7-a25b-90c5ec96b83b","added_by":"auto","created_at":"2026-04-22 02:25:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121741,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of employment institution types among clinical medicine graduates\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9062167/v1/b91b79f63aa15de57b58b4f8.jpg"},{"id":107480811,"identity":"c813ee13-787f-4080-8996-ad57dad3df68","added_by":"auto","created_at":"2026-04-22 02:13:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122477,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in initial monthly salary (CNY/month) among\u003c/p\u003e\n\u003cp\u003eclinical medicine master's graduates by employment flow pattern\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9062167/v1/9fe7714dfd9bdf421539674b.jpg"},{"id":107485572,"identity":"7557e3e6-bd5d-4a66-9f4e-4c3095f892d1","added_by":"auto","created_at":"2026-04-22 02:35:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2134883,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9062167/v1/ad2e57be-2ee4-4c00-947e-0575c9f4a506.pdf"},{"id":107101787,"identity":"3fb115c3-1abf-4fc5-b800-a279708ac15b","added_by":"auto","created_at":"2026-04-16 19:16:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":62722,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1Likertscale.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9062167/v1/3d71da391f55d067fb699771.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"What drives them home? Influencing factors of return migration to western China among clinical medicine master's graduates","fulltext":[{"header":"Research Background","content":"\u003cp\u003eEmployment is fundamental to people's livelihoods, and high-quality full employment serves as a crucial cornerstone of Chinese-style modernization\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Clinical medicine, as a key discipline for safeguarding population health and improving overall societal health levels, has talent cultivation and employment directly linked to the optimal allocation of the health workforce, forming an important foundation for formulating medical education policies and health manpower planning\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Medical postgraduate education spans both education and healthcare\u0026mdash;two major areas concerning people's well-being\u0026mdash;and constitutes the primary channel for cultivating high-level medical talent in China\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, China's medical workforce currently faces structural and regional imbalances. According to data from the 2023 China Health Statistical Yearbook, the proportion of licensed physicians with postgraduate degrees remained relatively low in 2022, with postgraduate degree holders accounting for only 19.00% of licensed physicians and 16.70% of licensed (assistant) physicians among health technicians\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, indicating an overall shortage of high-level medical talent. A more prominent issue lies in the regional imbalance of health workforce allocation. In western China in particular, the geographic distribution equity of licensed physicians is significantly lower. Although the total number of physicians nationwide has continued to grow, the heterogeneity of spatial distribution has intensified, with western provinces lagging in the allocation of high-quality physician resources, further widening regional disparities in healthcare service levels\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs the backbone of the future physician workforce, the employment patterns of clinical medicine master's graduates not only concern individual career development but also significantly influence the pattern and quality of the healthcare service system. Existing studies have focused largely on macrolevel dilemmas of \"insufficient quantity\" and \"severe outflow\" of health talent in western regions\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, with less attention given to the microlevel phenomenon of return migration among highly educated medical talent. More importantly, the current literature predominantly emphasizes surveys on graduates' return intentions\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e rather than examining actual return employment behaviors. According to the theory of planned behavior, behavioral intentions cannot fully predict actual behavior\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Investigating actual return employment behaviors can better reveal decision-making logic and influencing factors.\u003c/p\u003e \u003cp\u003eGiven this gap, the present study focuses on clinical medicine graduates who hold household registrations in western China, who have completed their master's education in nonwestern regions, and who ultimately returned to western China for employment\u0026mdash;defining them as the \"westward return migration group.\" This study systematically explores the characteristics of their employment location choices and the influencing factors. The findings will not only contribute to understanding the micromechanisms of return migration among high-level medical talent but also provide scientific evidence for optimizing the regional distribution of medical talent and improving the quality and efficiency of the health workforce structure in western China.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThe Opinions on Implementing the Employment Priority Strategy to Promote High-Quality Full Employment explicitly state the need to \"enhance the employment carrying capacity of coordinated regional development and promote balanced interregional employment development\"\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Within this policy context, the return migration of college graduates for employment\u0026mdash;defined as spatial mobility whereby graduates return from their place of study to their family location or region of origin for job seeking\u0026mdash;represents not only geographic return but also significant socioeconomic behavior, encompassing mechanisms such as human capital feedback, social network reconstruction, and regional development feedback\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Examining this phenomenon from the perspective of talent mobility is critically important for understanding the interactive relationship between individual development and regional coordination.\u003c/p\u003e \u003cp\u003eWhen this perspective is focused on the healthcare sector, the issue of talent mobility assumes more distinctive professional attributes and public value. The balanced regional allocation of health human resources serves as the cornerstone for achieving accessible, high-quality healthcare services\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. However, China has long faced the structural dilemma of simultaneous \"quantitative growth\" and \"regional imbalance\" in its health workforce, with the shortage of high-level clinical physicians in western regions being particularly pronounced\u0026mdash;a critical constraint on health equity\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Although some surveys indicate that a considerable proportion of medical master's graduates trained in western institutions secure employment within the region\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, broader research confirms that the \"outflow\" pressure on western physicians, especially core talent, to eastern coastal areas persists\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The coexistence of \"high retention rates\" and \"high outflow pressure\" reveals a deeper issue beyond the static question of \"whether they stay\": geographic retention does not necessarily equate to occupational stability or willingness for long-term settlement. Therefore, identifying and thoroughly investigating medical graduate groups with potentially greater intrinsic stability may represent the \"last mile\" in solving the challenge of \"retaining\" health talent in western China. Theoretically, groups who actively choose return migration may possess the potential to become highly stable cohorts, as their return decisions are often more deeply embedded in noneconomic factors such as local identity, emotional attachment, and family networks\u0026mdash;factors that may translate into stronger professional commitment. Simultaneously, their existing local social capital may help reduce adaptation costs and enhance employment stability \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, a review of the literature reveals certain limitations in both perspective and depth. First, the study population remains relatively broad, with current discussions largely focused on medical graduates as a whole or on primary health workers\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. There is a lack of specialized, fine-grained research targeting the core group of \"clinical medicine master's graduates\"\u0026mdash;a cohort characterized by both advanced education and a clear career orientation. Their mobility logic may differ fundamentally from that of undergraduate students or from that of contract-trained graduates. Second, the analytical perspective exhibits partial fragmentation: (a) in factor analysis, most studies list and test individual, family, and institutional factors as objective variables\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, neglecting the subjective perceptions and trade-off processes of graduates as active decision-makers in career choice; (b) in spatial analysis, existing research largely remains at the \"regional\" level, lacking a perspective that jointly examines \"interregional mobility\" and more fine-grained \"intercity mobility.\" Given the significant geographic imbalance within the western regions themselves\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, overlooking city-tier differences makes it difficult to address the true dilemma of internal imbalances in western talent distribution. These limitations hinder existing research from deeply analyzing the microlevel decision logic of active return migration among highly educated medical professionals, nor can it provide precise guidance for their optimal allocation within western regions.\u003c/p\u003e \u003cp\u003eTo address these research gaps, this study focuses on the return employment behavior of \"clinical medicine master's graduates with western China household registration.\" Using data from the 2024 National Survey on Education Satisfaction among Fresh Medical Postgraduate Graduates, this study aims to answer the following questions: (1) What pathway patterns emerge from \"place of study\" to \"place of employment\" among clinical medicine master's graduates returning to western China for employment? (2) In which tiers of medical institutions is this group concentrated in the western region? What characteristics do their employment quality (initial salary and employment satisfaction) exhibit? (3) What group characteristics define this returning cohort? What are the key factors driving their return behavior? By addressing these questions, this study aims to reveal the micromechanisms underlying the return migration of highly educated medical talent and provide scientific evidence for constructing a talent circulation in western regions that enables them to be \"attracted back, retained, and well utilized.\"\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eThis study utilized data from the National Survey on Education Satisfaction among Fresh Medical Postgraduate Graduates conducted from June to July 2024. The survey employed convenience sampling, with electronic questionnaires distributed online via the Wenjuanxing platform. Prior to implementation, the research team contacted faculty representatives at participating institutions through WeChat groups and 126 email accounts, who then distributed the questionnaire links and QR codes to students. The survey adhered to the principle of voluntary participation, implemented IP address restrictions to prevent duplicate responses, and obtained informed consent from all participants.\u003c/p\u003e \u003cp\u003eA total of 105 medical postgraduate training institutions were covered in the survey, including 56 in eastern China, 28 in central China, and 21 in western China. After data cleaning\u0026mdash;excluding nonfresh graduates, questionnaires with completion times of less than 360 seconds, and those with missing values\u0026mdash;17,476 valid questionnaires were retained for analysis.\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the factors influencing whether clinical medicine master's students in western China return to western China for employment after graduation. The inclusion criteria were full-time clinical medicine master's students whose family location was in western China and who had completed their studies in nonwestern regions. On the basis of their graduation destinations, the study population was divided into two groups: the \"westward return migration group\" and the \"nonreturn migration to western China group.\"\u003c/p\u003e \u003cp\u003eApplying these criteria, a total of 487 eligible graduates were identified, comprising 355 in the westward return migration group and 132 in the nonreturn migration group. All these genes were included in the final comparative analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables and measurements\u003c/h3\u003e\n\u003cp\u003eData were collected via a self-designed questionnaire. The employment module of the questionnaire comprises four sections: basic personal information, family characteristics, educational background, and graduation destinations. Specifically, basic personal information included gender and graduates' evaluations of factors influencing career choice. Participants rated the importance of 11 factors influencing their career choice on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;not at all important, 5\u0026thinsp;=\u0026thinsp;extremely important). The full scale is provided as Additional file 1.\u003c/p\u003e \u003cp\u003eThe family characteristics included household registration type, parental medical background, parental occupation, parental education level, and per capita annual household income. Educational background covered institution type, \"double first-class\" status, institutional adjustment, and degree type.\u003c/p\u003e \u003cp\u003eParental occupation was classified into three tiers according to the classification method of Yue\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e: (1) High-level occupations: administrative personnel (officials at the division level or county/township section level and above), senior managers in enterprises or public institutions, medical workers, private entrepreneurs, professional and technical personnel or technical support staff (technicians, etc.); (2) Middle-level occupations: general management and clerical staff, commercial and service personnel; (3) Grassroots-level occupations: self-employed individuals or freelancers, farmers (in agriculture, forestry, animal husbandry, and fishery), workers (production and transportation equipment operators), migrant workers, retirees, unemployed individuals, or other occupations. If both parents were engaged in grassroots-level occupations, this was categorized as \"both parents in grassroots occupations\"; otherwise, it was categorized as \"at least one parent in middle- to high-level occupations.\"\u003c/p\u003e \u003cp\u003eThe per capita annual household income was divided into three categories according to the same study\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e: low-income households (below 8,000 CNY), middle-income households (8,001\u0026ndash;29,000 CNY), and high-income households (29,001 CNY and above). The parental education level was classified on the basis of educational attainment: if neither parent had received a university education, the household was defined as having a low education level; otherwise, it was defined as having a high education level. Parental medical background was determined on the basis of occupational experience: if neither parent had experience in medical-related fields, this was considered no medical background; otherwise, it was considered medical background\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo measure regional disparities between graduates' places of study and employment, this study introduces two indicators: geographic distance and economic distance\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Geographic distance was represented by the shortest road distance between the provincial capital cities of the family location and the place of study (based on the shortest driving distance from AutoNavi). Economic distance was measured by the difference in per capita GDP between the provinces of the two locations.\u003c/p\u003e \u003cp\u003eIn the subsequent logistic regression analysis examining whether graduates returned to western China for employment, an unconditional binary logistic regression model was constructed. To comprehensively reflect the joint influence of \"geographic distance\" and \"economic distance\" on return behavior, this study constructed a \"comprehensive geographic‒economic disparity\" indicator, defined as the economic development level disparity per unit of geographic distance, measuring the comprehensive \"spatiotemporal‒economic\" barrier graduates must overcome when returning from the province of study to their home province. A larger value of this indicator indicates greater economic disparity per kilometer of geographic distance. To mitigate the impact of extreme values on the model and improve the skewed distribution of the variable, this indicator was logarithmically transformed after shifting. The specific calculation formula was as follows: comprehensive geographic‒economic disparity\u0026thinsp;=\u0026thinsp;log[(economic distance/geographic distance)\u0026thinsp;+\u0026thinsp;80.65].\u003c/p\u003e \u003cp\u003eIn the analysis of employment mobility patterns after returning to western China, drawing on existing studies on medical graduates choosing employment in their places of origin\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, the variable \"whether the city of employment is consistent with the place of origin\" was included in the multinomial logistic regression model to measure the influence of place of origin on clinical medicine graduates' employment location choices. Graduation destinations included graduates' employment locations, employing institutions, and employment quality.\u003c/p\u003e \u003cp\u003eThis study adopted the city tier classification released by the New First-Tier Cities Research Institute of China Business Network (CBN) in the *2024 New First-Tier Cities Attractiveness Ranking*\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Building on this and drawing on existing research practices\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, this study combined first-tier and new first-tier cities as \"first-tier cities,\" and combined third-tier, fourth-tier, and fifth-tier cities as \"third-tier and lower cities.\" Accordingly, city tiers were ultimately classified into three categories: first-tier, second-tier, and third-tier and below. In addition, per capita GDP data for each province were sourced from the officially released 2024 Statistical Bulletin on National Economic and Social Development of the respective provinces (autonomous regions and municipalities directly under the central government). The detailed variable assignments are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of explanatory variables and coding schemes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecific variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable description and reference group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndividual characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (reference) and male\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvaluation of factors influencing career choice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of permanent staffing, work location, institutional administrative level, position type, salary and benefits, professional relevance, marital status and partner, personal development, family expectations, institutional factors, mentor factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eFamily characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold registration type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural household registration (reference) and nonagricultural household registration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParental medical background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeither parent has medical background (reference) and at least one parent has medical background\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParental occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoth parents in grassroots occupations (reference) and at least one parent in mid-to-high-level occupations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParental education level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoth parents with low education level (reference) and at least one parent with high education level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer capita annual household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow income (reference), middle income, and high income\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducational characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent medical colleges (reference) and comprehensive universities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\"Double First-Class\" status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo (reference) and Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutional adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo (reference) and Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfessional degree (reference) and academic degree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRegional characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifference in per capita GDP between the province of the institution and the province of the family location\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeographic distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShortest road distance between the provincial capital cities of the institution and the family location (based on the shortest driving distance from AutoNavi)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComprehensive geographic-economic disparity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog[(economic distance/geographic distance)\u0026thinsp;+\u0026thinsp;80.65]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed via Excel 2010 and SPSS 26.0. Continuous data are presented as the means and standard deviations, whereas categorical data are expressed as frequencies and percentages. Chi-square tests and independent samples t tests were initially conducted to examine differences in the distribution of return migration among clinical medicine master's graduates with different characteristics. On this basis, an unconditional binary logistic regression model was employed to identify key factors influencing their return migration to western China for employment (α\u0026thinsp;=\u0026thinsp;0.05). Furthermore, for graduates who had returned to western China for employment, multinomial logistic regression was used to explore the factors influencing their city-tier mobility patterns (α\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003e1. Migration patterns of clinical medicine master's degree graduates with Western China household registration from the place of study to the place of employment\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(1) Characteristics of employment city distribution among clinical medicine master's graduates with Western China household registration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, among the return migrants employed in western China, nearly half (49.57%) of the graduates flowed to third-tier and lower-tier cities, constituting the most common type of employment city choice, followed by first-tier cities, accounting for 40.85%. In contrast, the nonreturn migration group was highly concentrated in first-tier cities, with a proportion reaching 50.00%, whereas the employment proportions in second-tier cities and third-tier and lower-tier cities decreased successively, at 31.82% and 18.18%, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of employment city tiers and flow patterns among clinical medicine master's graduates with Western China household registration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistribution of employment city tier\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eReturn migration group\u003c/p\u003e \u003cp\u003e(355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145 (40.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFirst-tier cities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (9.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecond-tier cities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176 (49.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThird-tier and lower-tier cities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNonreturn migration group (132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFirst-tier cities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (31.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecond-tier cities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (18.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThird-tier and lower-tier cities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(2) Characteristics of intercity mobility patterns among clinical medicine master's graduates with Western China household registration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the urban mobility patterns of the return-migration group exhibited an overall characteristic of \"stability in third-tier cities and differentiation in second-tier cities.\" Specifically, parallel mobility (referring to cases where the city tier of the place of study was the same as that of the place of employment) in third-tier and lower-tier cities had the highest proportion, reaching 11.27%. Among clinical medicine master's graduates trained in second-tier cities in nonwestern regions, employment location choices displayed a \"polarized\" pattern: 29.58% flowed to first-tier cities in western China, whereas 29.30% flowed to third-tier and lower-tier cities in western China.\u003c/p\u003e \u003cp\u003eIn contrast, the urban mobility patterns of the nonreturn migration group were predominantly characterized by \"stability in first-tier cities and upgrading from second-tier cities.\" Among this group, parallel mobility in first-tier cities constituted the highest proportion, at 24.24%. Graduates from second-tier cities in nonwestern regions were more inclined to seek employment in first-tier cities in nonwestern regions, demonstrating a clear tendency toward upward mobility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Employment institutions and employment quality of clinical medicine master's graduates with Western China household registration\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(1) Characteristics of the employment institution tier distribution among clinical medicine master's graduates with Western China household registration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, hospitals were the primary employment destinations for clinical medicine master's graduates (including both return migration and nonreturn migration groups). Further analysis of the 411 graduates who chose hospitals (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details) revealed that their employment institution tiers were highly concentrated in tertiary Grade A hospitals, a characteristic consistently observed in both the return migration and nonreturn migration groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of employment hospital tiers among graduates of clinical medicine master's degrees\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital tier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReturn migratione (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNonreturn migratione (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary Grade A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e314(91.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97(78.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary Grade B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16(4.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22(17.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary Grade C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary Grade A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNongraded hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e(2) Differences in employment quality among clinical medicine master's graduates with Western China household registration\u003c/h3\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, regarding initial salary, the distribution exhibited significant differences between return migrants and nonreturn migrants. A greater proportion of return migrants were in the 3,000\u0026ndash;5,000 CNY range, whereas the two groups were roughly comparable in the 5,001\u0026ndash;10,000 CNY range. However, in the high-end range of 10,001 CNY and above, the proportion of nonreturn migrants was significantly greater than that of return migrants. This difference in initial salary structure was directly reflected in employment satisfaction, with nonreturn migrants perceiving significantly stronger satisfaction with salary and benefits (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for details), a finding that stands in stark contrast to the advantage of this group in the high initial salary segment shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eclinical medicine master's graduates by employment flow pattern\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in employment satisfaction among clinical medicine master's graduates by employment flow pattern\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReturn migration\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNonreturn migration\u003c/p\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmploying institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalary and benefits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal development space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCareer prospects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Factors influencing return to western China among clinical medicine master's graduates with Western household registration\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(1) Descriptive analysis of factors influencing return to western China among clinical medicine master's graduates with Western household registration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGraduates whose parents both had no medical background, both were engaged in grassroots occupations, and held professional degrees presented higher proportions of return migration than those whose parents had at least one medical background or were in middle- to high-level occupations, as well as those with academic degrees (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, graduates who placed greater importance on work location and family expectations were more likely to return to western China for employment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The smaller the comprehensive geographic‒economic disparity is, the stronger the tendency for graduates to return to western China for employment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas graduates who place greater emphasis on mentor factors are more inclined toward nonreturn migration (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eWith respect to other factors, male graduates, those with agricultural household registration, those with parents with lower education levels, those with lower per capita annual household income, those graduating from independent medical colleges or non\"double first-class\" universities, those who had experienced institutional adjustment, and those who placed greater importance on factors such as the availability of permanent staffing, institutional and position type, salary and benefits, professional relevance, and personal development presented a greater tendency toward return migration to western China. Conversely, those who placed relatively less importance on institutional factors were more likely to choose nonreturn migration. However, none of these differences reached statistical significance. The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReturn migration status among clinical medicine master's degrees with different characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReturn migration group [n(%)/Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNonreturn migration group [n(%)/Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ2/t value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239(72.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90(27.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116(73.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42(26.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold registration type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211(75.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67(24.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonagricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144(68.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65(31.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental medical background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeither parent has medical background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e324(74.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112(25.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt least one parent has medical background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31(60.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20(39.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth parents in grassroots occupations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281(76.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86(23.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt least one parent in mid-to-high-level occupations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74(61.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46(38.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental education level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth parents with low education level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293(74.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99(25.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt least one parent with high education level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62(65.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33(34.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer capita annual household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130(73.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48(26.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134(72.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50(27.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91(72.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34(27.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitution type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent medical college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219(73.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80(26.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136(72.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52(27.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"Double First-Class\" status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227(74.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78(25.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128(70.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54(29.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198(69.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85(30.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157(76.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47(23.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294(75.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95(24.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61(62.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37(37.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation of factors influencing career choice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailability of permanent staffing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional nature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosition type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalary and benefits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional relevance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status and partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily expectations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMentor factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographic distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1570.83\u0026thinsp;\u0026plusmn;\u0026thinsp;687.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1661.05\u0026thinsp;\u0026plusmn;\u0026thinsp;781.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33444.17\u0026thinsp;\u0026plusmn;\u0026thinsp;30458.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48469.10\u0026thinsp;\u0026plusmn;\u0026thinsp;42153.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive geographic-economic disparity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.32\u0026thinsp;\u0026plusmn;\u0026thinsp;34.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.74\u0026thinsp;\u0026plusmn;\u0026thinsp;44.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(2) Unconditional binary logistic regression analysis of factors influencing return migration to western China among clinical medicine master's graduates with Western household registration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst, whether clinical medicine master's degree graduates returned to Western China for employment was used as the binary dependent variable (0\u0026thinsp;=\u0026thinsp;no, 1\u0026thinsp;=\u0026thinsp;yes). All the aforementioned variables were subsequently included in the regression model, and univariate analysis was conducted via the \"Enter\" method. Given that a strict significance level in univariate analysis might lead to the exclusion of some statistically meaningful variables, the significance threshold for univariate analysis was relaxed to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in this study, meaning that variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were considered to have a statistical association with the outcome variable. Finally, variables selected from the univariate analysis that were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) were included in the model for multivariate analysis.\u003c/p\u003e \u003cp\u003eThe regression analysis results indicated that, from the perspective of individual characteristics, family expectations, mentor factors, and degree type significantly influenced the return migration of clinical medicine master's graduates with western household registration to western China. For each unit increase in the importance of graduates attached to family expectations, the odds of returning to western China for employment increased by a factor of 1.33 (OR\u0026thinsp;=\u0026thinsp;1.33, 95% CI: 1.01\u0026ndash;1.76). In contrast, for each unit increase in the importance attached to mentor factors, the odds of return migration decreased to 0.68 times the original value (OR\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.55\u0026ndash;0.86). Furthermore, compared with graduates with professional degrees, those with academic degrees had odds of return migration that were only 0.52 times greater (OR\u0026thinsp;=\u0026thinsp;0.52, 95% CI: 0.31\u0026ndash;0.88). At the family background level, graduates with at least one parent engaged in middle- to high-level occupations had odds of return migration that were only 0.51 times greater than those of graduates with both parents in grassroots occupations (OR\u0026thinsp;=\u0026thinsp;0.51, 95% CI: 0.27\u0026ndash;0.95). From the perspective of regional characteristics, for each unit increase in the comprehensive geographic‒economic disparity, the odds of graduates returning to western China for employment decreased to 0.47 times the original value (OR\u0026thinsp;=\u0026thinsp;0.47, 95% CI: 0.27\u0026ndash;0.82). The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnconditional binary logistic regression results for factors influencing return migration to western China among clinical medicine master's graduates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold registration type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonagricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u0026thinsp;~\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental medical background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least one parent has medical background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeither parent has medical background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u0026thinsp;~\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least one parent in mid-to-high-level occupations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoth parents in grassroots occupations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u0026thinsp;~\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental education level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least one parent with high education level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoth parents with low education level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u0026thinsp;~\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u0026thinsp;~\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfessional degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u0026thinsp;~\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAvailability of permanent staffing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u0026thinsp;~\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWork location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u0026thinsp;~\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePersonal development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u0026thinsp;~\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFamily expectations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u0026thinsp;~\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMentor factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u0026thinsp;~\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eComprehensive geographic-economic disparity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u0026thinsp;~\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(3) Multinomial logistic regression analysis of factors influencing intercity employment patterns among graduates of clinical medicine master's programs with Western household registration returning to western China\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUsing intercity employment mobility patterns as the dependent variable, a multinomial logistic regression model based on the forward:LR method was employed to explore the factors influencing the intercity employment mobility patterns of clinical medicine master's graduates who returned to western China for employment. The factors associated with different employment mobility patterns included \"double first-class\" status, whether the employment city was the same as the place of origin, and institutional factors in the evaluation of career choice influences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe results revealed that graduation from a \"double first-class\" university, whether the employment city was the same as the place of origin, and the evaluation of institutional factors in career choice (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were significantly associated with employment mobility patterns. Graduates from \"Double First-Class\" institutions were 2.89 times more likely to choose parallel mobility (OR\u0026thinsp;=\u0026thinsp;2.89, 95% CI: 1.56\u0026ndash;5.36) and 2.91 times more likely to choose downward mobility (OR\u0026thinsp;=\u0026thinsp;2.91, 95% CI: 1.65\u0026ndash;5.13) than to choose upward mobility. In contrast, graduates who prioritized institutional factors were more inclined to choose upward mobility (OR\u0026thinsp;=\u0026thinsp;0.59, 95% CI: 0.45\u0026ndash;0.77; OR\u0026thinsp;=\u0026thinsp;0.74, 95% CI: 0.58\u0026ndash;0.95). Furthermore, among clinical medicine master's graduates whose employment city was consistent with their place of origin, the likelihood of choosing downward mobility was 4.46 times greater than that of choosing upward mobility (OR\u0026thinsp;=\u0026thinsp;4.46, 95% CI: 2.62\u0026ndash;7.58). The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial logistic regression analysis of factors influencing intercity employment mobility pattern choices among clinical medicine master's graduates returning to western China\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eParallel mobility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDownward mobility\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"Double First-Class\" status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.89(1.56\u0026thinsp;~\u0026thinsp;5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.91(1.65\u0026thinsp;~\u0026thinsp;5.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment city consistent with place of origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37(0.74\u0026thinsp;~\u0026thinsp;2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.50**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.46(2.62\u0026thinsp;~\u0026thinsp;7.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eInstitutional factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.53**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59(0.45\u0026thinsp;~\u0026thinsp;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.30*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74(0.58\u0026thinsp;~\u0026thinsp;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Note: *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusions and Discussion","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cp\u003eOn the basis of data from the 2024 National Survey on Education Satisfaction among Fresh Medical Postgraduate Graduates, this study systematically analyzed the return employment behavior of clinical medicine master's graduates with Western China household registration. The main findings are as follows:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e(1) Conclusions\u003c/h3\u003e\n\u003cp\u003eFirst, clinical medicine master's graduates returning to western China exhibited a \"dumbbell-shaped\" urban distribution pattern, with consistency between the place of origin and employment city serving as the core driver of downward mobility. This study revealed that the distribution of employment city tiers among this group displayed pronounced bipolar characteristics: first-tier cities (48.85%) and third-tier and lower-tier cities (49.57%) accounted for the highest proportions, whereas second-tier cities (9.58%) were notably underrepresented, forming a \"dumbbell-shaped\" structure. This stands in stark contrast to the \"pyramid-shaped\" distribution observed in the nonreturn migration group, which decreased progressively from higher to lower tiers (first-tier 50.00% \u0026gt; second-tier 31.82% \u0026gt; third-tier 18.18%). Contrary to findings from existing studies\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, the proportion of returning graduates who flow to third-tier and lower-tier cities in this study was relatively high. Further analysis of mobility directions revealed that graduates from \"Double First-Class\" universities were more inclined toward parallel or downward mobility, whereas graduates who placed greater emphasis on institutional factors were more likely to choose upward mobility. This phenomenon is related to the reality that western China has a limited number of first- and second-tier cities and a relatively concentrated urban system structure. According to statistics, first- and second-tier cities in western China account for only 2.08% of all prefecture-level cities in China and are predominantly provincial capitals\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. This regional structural constraint objectively limits graduates' employment options in developed cities within western China, leading those with higher expectations of institutional brand value to be more inclined toward first-tier cities to maximize returns on their educational investment. Graduates whose employment city was consistent with their place of origin were 4.46 times more likely to choose downward mobility than upward mobility, highlighting the critical role of place identity in return migration decisions. Scannell and Gifford noted that individuals' emotional attachment to their places of growth influences their spatial behavior decisions through social bonds and cultural identity mechanisms\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The high proportion of employment in third-tier and lower-tier cities reflects the driving effect of local embeddedness: the stock of social capital provided by places of origin (family networks, peer relationships, etc.) can effectively reduce risk and uncertainty in the early career stages, whereas graduates' place identity also makes them more willing to return to familiar SMEs for development.\u003c/p\u003e \u003cp\u003eSecond, comprehensive geographic‒economic disparity significantly inhibits return migration, with regional development gaps constituting a key barrier to talent return migration. The greater the \"economic disparity per unit of geographic distance\" between the place of study and the place of origin is, the lower the likelihood of graduates returning. The descriptive statistics indicate that the mean economic disparity in the return migration group (33,444.17 CNY) was significantly lower than that in the nonreturn migration group (48,469.10 CNY, t\u0026thinsp;=\u0026thinsp;3.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that regional economic differences are an important factor influencing talent returns. Domestic research has also confirmed that the greater the economic disparity between the place of study and the place of origin is, the lower the probability of graduates returning\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. With respect to employment institution type, clinical medicine master's graduates were highly concentrated in tertiary Grade A hospitals, regardless of whether they returned to Western China. In terms of salary and satisfaction, returnees in western China generally had lower high-end salary levels and lower levels of satisfaction with salary and benefits than nonreturnees did.\u003c/p\u003e \u003cp\u003eThird, family factors, educational factors, and mentor factors exerted differential effects on return migration decisions. Family expectations and parents engaged in grassroots occupations had significantly positive effects on return migration, whereas mentor factors had significantly negative effects, revealing the push‒pull dynamics that graduates face between their \"family-of-origin networks\" and their \"academic mentorship networks.\" Different networks impose competing constraints on individual behavior, with students who are more dependent on their mentors being more inclined to develop in their mentors' locations. Furthermore, graduates with professional degrees showed significantly greater return tendencies than those with academic degrees did, reflecting fundamental differences in the training objectives of the two degree types: professional degrees focus on clinical practice competence, with professional activities strongly dependent on the local healthcare environment; academic degrees emphasize scientific research innovation competence, typically requiring reliance on high-level research platforms, where the concentration of high-quality resources in economically developed regions creates stronger regional attractiveness.\u003c/p\u003e\n\u003ch3\u003e(2) Discussion\u003c/h3\u003e\n\u003cp\u003eOn the basis of the above findings, this study proposes the following policy recommendations at the micro, meso, and macro levels:\u003c/p\u003e \u003cp\u003eFirst, at the micro level, local governments and healthcare institutions can establish targeted talent attraction mechanisms to effectively activate \"place identity.\" International studies have shown that place identity is a key factor influencing healthcare workers' retention intentions, with retention rates being significantly higher among healthcare workers whose place of origin is consistent with their employment location\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Therefore, consistency between place of origin and employment location can be used as an important predictor of stability and retention. A database of medical talent with local household registration should be established, enabling early engagement with local medical students studying elsewhere. Through emotional connections and a sense of belonging, such engagement can guide their return migration and enhance the likelihood of their long-term service in the local area.\u003c/p\u003e \u003cp\u003eSecond, at the meso level, universities should optimize medical talent training models to increase students' \"local embeddedness.\" Given the higher return tendency observed among graduates with professional degrees, it is recommended that local-oriented components be integrated into the training process. For example, some clinical rotations could be arranged at collaborating hospitals in western regions. Additionally, a dual mentorship system comprising \"academic mentors\u0026thinsp;+\u0026thinsp;local career mentors\" could be explored, providing simultaneous academic support and local career network connections for students interested in returning. This would help them establish themselves in local areas while maintaining academic linkages.\u003c/p\u003e \u003cp\u003eThird, at the macro level, the government should promote coordinated regional development, narrow regional economic disparities, and reduce the \"opportunity cost\" of talent return migration. Drawing on the experience of Australia's \"Rural Medical Incentive Program,\" combining salary subsidies with career development support has been shown to significantly improve physician retention rates in remote areas\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Given the significant inhibitory effect of comprehensive geographic‒economic disparity on return migration, it is recommended that special position subsidies be provided for master's-level talent working in third-tier and lower-tier cities. Through salary compensation mechanisms, their overall income should be maintained at no less than a certain proportion of comparable positions in eastern regions, thereby enhancing western regions' attractiveness to high-level medical talent.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e(3) Limitations and future directions\u003c/h2\u003e \u003cp\u003eThis study has several limitations that warrant further improvement in future research.\u003c/p\u003e \u003cp\u003eFirst, with respect to the sampling method, this study employed convenience sampling. Although this approach offers certain advantages in terms of sample coverage, it may introduce bias in accurately reflecting the characteristics of the entire population of clinical medicine master's graduates returning to western China for employment. The proportion of graduates from some western provinces, such as Tibet and Qinghai, was relatively low in the sample, which may inadequately capture the return migration characteristics of medical talent from these regions. Moreover, the western provinces exhibit significant heterogeneity in economic development levels, ethnic composition, and healthcare resources. The imbalanced sample structure may compromise the representativeness and external validity of the study findings. Future research could adopt stratified sampling or quota sampling to ensure better structural balance across dimensions such as province, institution, and city tiers, thereby enhancing the generalizability of the conclusions.\u003c/p\u003e \u003cp\u003eSecond, regarding variable measurement, the conceptual breadth of certain key variables, without further subdivision, may have compromised the precision of the analysis. For example, \"mentor factors,\" identified as important variables influencing return migration decisions, were measured only by the importance graduates attached to them, without distinguishing subdimensions such as mentors' supervisory style, resource networks, or geographic origins. Similarly, \"family expectations\" were not further disaggregated into different dimensions, such as \"emotional support\" versus \"economic dependence.\" Furthermore, this study analyzed clinical medicine as an aggregate whole without further distinguishing its secondary disciplinary directions. In reality, different specialties\u0026mdash;such as internal medicine, surgery, obstetrics and gynecology, and pediatrics\u0026mdash;may exhibit variations in job market supply and demand, policy support intensity, and individual geographic preferences. Future research could adopt more refined measurements of key variables and conduct in-depth comparisons at the specialty level to increase analytical precision and explanatory power.\u003c/p\u003e \u003cp\u003eThird, regarding the research design, this study was based on cross-sectional survey data, which can describe employment status and related factors at a single point in time but cannot reveal the dynamic process of returnees' career development or long-term stability. Cross-sectional data cannot capture postreturn career adaptation, intentions for remigration, or actual mobility behaviors, nor can they determine whether \"returns\" truly equate to \"rootedness.\" Subsequent research could adopt longitudinal designs and collect data at multiple time points to explore the mechanisms sustaining medical talent return to western China thoroughly, career progression pathways, and remigration trends. Additionally, future studies could extend the research population to related health professions, such as nursing and public health, and conduct interdisciplinary comparative research to develop more universally applicable strategies for attracting health professionals to western regions. Furthermore, mixed-methods approaches could be considered, incorporating qualitative interviews to gain deeper insights into the subjective motivations underlying return decisions, thereby complementing the limitations of quantitative data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003e This study strictly adhered to the ethical guidelines of the Declaration of Helsinki and the principles of data confidentiality. This study was approved by the Ethics Committee of Peking University under the ethical approval number IRB00001052\u0026ndash;20074. All participants provided informed consent, and the research was conducted in full compliance with established ethical guidelines and data confidentiality principles.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eAll the authors declare that they have no competing interests to report.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQS Y: Responsible for data processing and analysis, as well as paper writing; MT Z, ZQ W, JL,ZS L, Y J: Participated in paper revision; JZ J: Provided guidance on research design and paper revision.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eWe would like to express our sincere gratitude to all the members of the research team for their valuable suggestions and continuous support throughout the paper writing process.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLAI DS. 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Rural Remote Health. 2012;12:1900.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clinical medicine master's graduates, Employment in western China, Return migration, Influencing factors","lastPublishedDoi":"10.21203/rs.3.rs-9062167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9062167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground To explore the group characteristics, mobility patterns, and key influencing factors associated with the return migration of clinical medicine master's graduates with western China household registration to employment in western China, this study providesevidence for optimizing medical workforce allocation in these areas.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods Data were derived from the 2024 National Survey on Education Satisfaction among Fresh Medical Postgraduate Graduates. Using convenience sampling, 487 eligible graduates were selected as study subjects. Chi-square tests, t tests, and logistic regression models were employed to systematically analyze employment flow patterns and related influencing factors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults The return migration group exhibited a city distribution pattern characterized by \"stability in third-tier cities and differentiation in second-tier cities,\" with nearly half (49.57%) choosing employment in third-tier or lower-tier cities. Multivariate logistic regression revealed that family expectations (OR=1.33, 95% CI: 1.01--1.76) served as a significant pull factor for return migration. In contrast, the mentor factor (OR=0.68, 95% CI: 0.55--0.86) and comprehensive geographic‒economic disparity (OR=0.47, 95% CI: 0.27--0.87) constituted major barriers. Graduates with academic degrees were 0.52 times as likely to return as those with professional degrees were (OR=0.52, 95% CI: 0.31--0.88). Graduates with at least one parent in middle- to high-level occupations were 0.51 times as likely to return as those with both parents in grassroots occupations were (OR=0.51, 95% CI: 0.27--0.95). With respect to intercity mobility patterns, consistency between place of origin and employment location emerged as a key factor: when consistent, the odds of graduates choosing downward mobility were 4.46 times greater than those choosing upward mobility (OR=4.46, 95% CI: 2.62--7.58).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions The return employment behavior of clinical medicine master's graduates results from a complex interplay of emotional ties, social capital, and regional economic disparities. Among these factors, the consistency between place of origin and employment location plays a dominant role in the choice of city-tier mobility patterns.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"What drives them home? Influencing factors of return migration to western China among clinical medicine master's graduates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 19:15:53","doi":"10.21203/rs.3.rs-9062167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-08T09:37:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T09:46:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T17:54:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-16T02:57:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-03-16T02:52:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dc6ba487-4532-44e5-ad79-5623652440c6","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T19:15:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 19:15:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9062167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9062167","identity":"rs-9062167","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}