Background
The level of nursing human resources is a key link in healthcare, and its rationality is directly related to the quality and efficiency of healthcare services [1]. With the aging of the global population and the increase in the number of patients with chronic diseases, there is a growing demand for professional nursing services, requiring that nursing human resources must be allocated in a more scientific and rational manner [2, 3]. In addition, advances in medical technology, improvements in nursing education and training systems, and the formulation of relevant policies and regulations are all important factors that affect the allocation of nursing human resources [4, 5]. Therefore, an in-depth study of nursing human resources not only improves the overall level of healthcare services, but also promotes the development of the nursing profession to meet the challenges of public health, which is essential for building an efficient and sustainable healthcare system.
Research on nursing human resources by scholars at home and abroad has mainly focused on three aspects: first, research on the supply and demand situation, which has analyzed the allocation level of nursing human resources from the macro national level [6, 7], the meso provincial level [8, 9], and the micro urban level [10, 11], and considered the impact of population aging, changes in the disease spectrum, regional and urban-rural differences, and healthcare technology updates, and other socioeconomic factors that potentially affect the demand for nursing care [12‐14]. Second, the research on allocation efficiency, by constructing an evaluation system [15, 16], analyzing the current situation of nursing human resources in hospitals [17], exploring the impact of factors such as nursing staff size, staffing patterns, nursing workload and cost-effectiveness on nursing efficiency [18, 19], and proposing strategies to optimize the layout of nursing human resources [20]. Finally, research on management policies and innovative practices, it is generally recognized that the development of a training system that is closely aligned with job requirements is essential for improving nurses’ clinical nursing service capabilities [21]. This job-centered training approach ensures that nurses demonstrate excellent professional skills and service quality in clinical practice, thereby improving the overall standard of nursing care delivery. Further studies have also emphasized the importance of planning clear career pathways for nursing staff [22, 23]. Such pathway planning not only motivates nursing staff, but also enhances their loyalty to the profession. By providing clear promotion channels and personal development opportunities, the job satisfaction of nursing staff can be effectively enhanced, which in turn increases their retention rate. In addition, a reasonable compensation incentive mechanism has been shown to be extremely critical in attracting and retaining excellent nursing talent [24, 25]. A fair and competitive compensation system with personalized incentive policies can significantly increase nursing staff’s motivation and commitment to their careers. In the practice of management innovation, studies have proposed the establishment of a vertical management system from the nursing department to the chief nurse of the ward to the head nurse and the implementation of a three-tier performance management model [26, 27]. The purpose of this model is to achieve basic authorization and dual-track deployment, thus giving nurses greater autonomy. This not only enhances the flexibility and responsiveness of nursing services, but is also an important strategy to adapt to the development needs of the times. In summary, the academic community has already conducted in-depth empirical studies around nursing human resources and provided useful insights for promoting the balanced development of nursing human resources and the strategy of a healthy China. Based on the analysis of existing literature, this study found that there is still some room for research on this topic. First, although existing studies have revealed regional differences in nursing human resources across geographic and socioeconomic dimensions, these studies often fail to delve into the specific sources and causes of these differences. Second, existing studies have largely ignored the impact of spatial factors on nursing human resource allocation and the dynamic convergence process of nursing human resource allocation levels in different regions over time.
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The Yangtze River Economic Belt covers a vast area from the east to the west of China, including 11 provinces and cities. With the aging of the population and the increase in healthcare demand, the rational allocation and optimization of nursing human resources have become the key to improving the regional health service level. The rapid development of the Yangtze River Economic Belt has brought about changes in the demographic structure and healthcare demand, and the uneven regional distribution of nursing human resources, as an important part of health services, may affect the equity and efficiency of healthcare services. This study aims to explore the status of inter-regional differences in the level of nursing human resources in the Yangtze River Economic Belt and the trend of development gaps, with a view to providing policy makers with decision-making references.
To achieve this goal, this study introduces the Dagum Gini coefficient not only to measure the overall inequality, but also to decompose it into three parts: intra-regional differences, inter-regional differences, and hypervariable density, which provides a more detailed perspective for revealing the imbalance in nursing human resource allocation; Meanwhile, this study constructs a convergence model that includes time series and spatial effects, aiming to explore the long-term trend and dynamic convergence process of nursing human resources allocation levels in different regions. By integrating the temporal and spatial dimensions, a comprehensive framework for assessing the dynamic evolution of nursing human resource allocation can be realized. These findings have important theoretical and practical significance for the formulation of more accurate nursing human resource allocation policies, which will help promote the equalization and optimization of nursing services and thus support the promotion of the “Healthy China” strategy.
Methods
Data sources
This paper takes 114 prefecture-level cities in 11 provinces of the Yangtze River Economic Belt as the research object. To conduct a more targeted heterogeneity study, the study area is divided into three regions: upstream, midstream, and downstream according to the Outline of the Development Plan for the Yangtze River Economic Belt, which the upstream of the Yangtze River Economic Belt contains Chongqing Municipality, 21 prefectural-level municipalities of Sichuan Province, 9 prefectural-level municipalities of Guizhou Province, and 8 prefectural-level municipalities of Yunnan Province; the midstream contains 10 prefectural-level municipalities of Jiangxi Province, 16 prefectural-level municipalities of Hubei Province, and 13 prefectural-level municipalities of Hunan Province; the downstream Containing Shanghai, 12 prefecture-level cities in Jiangsu Province, 15 prefecture-level cities in Zhejiang Province, and 10 prefecture-level cities in Anhui Province. Relevant data from the 2011–2021 China Urban Statistical Yearbook, provincial statistical yearbooks, national economic and social development statistical bulletin, etc. Yunnan Province data is incomplete, missing data excluded, and the rest of the missing data using linear interpolation to make up for the missing data.
Indicator selection
Combining the actual situation of nursing human resources and referring to existing studies [28, 29], the number of registered nurses in each prefecture-level city is selected as an indicator to measure the level of nursing human resources.
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Research methods
Dagum Gini coefficient measurement and decomposition
The Dagum Gini coefficient decomposition method was proposed by Dagum (1997) [30], which can effectively take into account the distribution of subgroup samples and the cross overlap between the sample data to realize the decomposition of the sources of differences, so as to overcome the shortcomings of the traditional Gini coefficient and Terrell’s coefficient. In this paper, Dagum Gini coefficient and subgroup decomposition are utilized to portray the regional differences of nursing human resources in China’s Yangtze River Economic Belt. The calculation formula is as follows:
$$G\, = \,{{\sum\nolimits_{{\rm{j}}\, = \,1}^{\rm{k}} {\sum\nolimits_{h\, = \,1}^k {\sum\nolimits_{i\, = \,1}^{nj} {\sum\nolimits_{r\, = \,1}^{nh} {\left| {{y_{ji}}\, - \,{y_{hr}}} \right|} } } } } \over {2{n^2}\bar y}}$$
where G denotes the overall Gini coefficient, the larger G indicates the larger regional disparity in nursing human resources in the Yangtze River Economic Belt, n denotes the number of all cities, k denotes the number of regions into which the Yangtze River Economic Belt is divided, nj (nh) denotes the number of cities within the j(h) region, and yji (yhr) denotes the number of registered nurses in the i(r) provinces within the j(h) region, which refers to the average of the 114 cities in terms of nursing human resources. The Dagum Gini coefficient is decomposed into Gw, Gnb and Gt, and the relationship between the three satisfies GDagum =Gw + Gnb + Gt, which represent intra-region variance, inter-region variance, and hypervariance density, respectively.
Convergence model
Convergence originates from the economic convergence phenomenon in neoclassical economics, including σ convergence and β convergence [31]. In this study, σ convergence is used to measure the degree of spatial dispersion in the level of nursing human resources in the Yangtze River Economic Belt in order to determine whether there is a trend towards balanced development.The existence of σ convergence indicates that the differences in the level of nursing human resources between regions in the Yangtze River Economic Belt have narrowed over time, pointing to the possibility of integration of the level of human resources in the region. Further, β-convergence is introduced as to whether regions with lower levels of nursing human resources grow at a faster rate to catch up with regions with higher levels of human resources. β-convergence is categorized into absolute β-convergence and conditional β-convergence. Absolute β-convergence assumes a tendency to equalize the level of nursing human resources across regions in the state of nature. Conditional β-convergence, on the other hand, tests whether convergence still exists after controlling for external variables. In order to analyze the convergence characteristics in depth, drawing on existing literature [32, 33], the following control variables were included: the level of economic development (gdp), which is measured by the gross regional product (million yuan), reflecting the economic strength of the region; the standard of living of the residents (urban), which is measured by the average salary of the employees (yuan), representing the economic status of the residents; and Healthcare resources (birth), assessed through the number of hospital and health center beds (sheets), reflect the level of healthcare services; and the level of education (open), measured by education expenditures, indicative of the distribution of regional education resources. The comprehensive consideration of these control variables aims to provide a scientific basis for accurately assessing the convergence characteristics of nursing human resources in the Yangtze River Economic Belt and for related policy formulation.
Results
Changes in nursing human resources in the Yangtze River Economic Belt
Table 1 demonstrates the changes in the total nursing human resources in the Yangtze River Economic Belt in general and in the upstream, midstream and downstream between 2010 and 2020. It can be seen that the total amount of nursing human resources in the downstream is much larger than that in the middle and upstream. From 2010 to 2020, the number of nursing human resources in the Yangtze River Economic Belt and the upper, middle and lower reaches of the Yangtze River Economic Belt increased significantly, and with 2010 as the base, the average annual growth rates of the number of nursing human resources in the Yangtze River Economic Belt and the upper, middle and lower reaches of the Yangtze River Economic Belt were 9.60%, 11.45%, 9.46%, and 8.68%, respectively, with the upstream having the fastest growth rate, and the middle and lower reaches growing at a lower rate than the overall average.
Table 1
Number of registered nurses in the Yangtze River Economic Belt in general and in the upper, middle and lower reaches (10,000)
Particular year | Yangtze River Economic Belt in general | Upper reaches | Middle reaches | Lower reaches |
|---|---|---|---|---|
2010 | 81.24 | 17.83 | 26.48 | 36.93 |
2011 | 85.99 | 20.56 | 26.61 | 38.82 |
2012 | 96.71 | 23.83 | 30.16 | 42.72 |
2013 | 108.22 | 27.22 | 33.20 | 47.80 |
2014 | 118.41 | 30.52 | 36.49 | 51.40 |
2015 | 128.99 | 33.66 | 39.31 | 56.02 |
2016 | 140.10 | 37.10 | 43.22 | 59.78 |
2017 | 152.91 | 41.13 | 46.01 | 65.77 |
2018 | 165.72 | 45.15 | 50.00 | 70.57 |
2019 | 181.32 | 49.51 | 55.83 | 75.98 |
2020 | 203.02 | 52.74 | 65.37 | 84.91 |
Average annual growth rate (%) | 9.60% | 11.45% | 9.46% | 8.68% |
Dagum Gini coefficient difference analysis of nursing human resources in the Yangtze River Economic Belt
In terms of the overall level of disparity, between 2010 and 2020, the regional disparity in nursing human resources in the Yangtze River Economic Belt showed a slight upward and downward trend, with the Gini coefficient increasing from 0.409 to 0.422, indicating that the imbalance in nursing human resources within the region has widened. This phenomenon can be attributed to the large differences in the economic development of the provinces within the economic belt, with more developed regions having more resources to invest in the cultivation of nursing human resources, while less developed regions are constrained by limited resources, making it difficult to effectively improve the level of nursing human resources. As shown in Table 2; Fig. 1.
In terms of intra-regional differences, between 2010 and 2020, the gap in nursing human resources levels in the Yangtze River Economic Belt was greatest in the upstream regions, and smaller in the middle and downstream regions. In terms of the specific evolution process, the overall gap in nursing human resources in the midstream region has been in a stable equilibrium; the gap in nursing human resources in the downstream region shows a fluctuating upward trend, with the Gini coefficient rising from the lowest point of 0.338 to 0.393. The Gini coefficient of the upstream region has dropped from 0.491 to 0.475, experiencing the process of decline, then rise and then slow decline, and the decline is the most obvious. As shown in Fig. 2.
Table 2
Regional Gini coefficients of nursing human resources in the Yangtze River Economic Belt and their decomposition results
Particular year | Total | Intra-regional variations | Interregional differences | Contribution (%) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
Upper reaches | Lower reaches | Middle reaches | Upper reaches&Lower reaches | Upper reaches&Middle reaches | Lower reaches&Middle reaches | Regional | Intra-regional | Hypervariable density | ||
2010 | 0.409 | 0.491 | 0.337 | 0.367 | 0.472 | 0.466 | 0.365 | 31.54% | 16.80% | 51.66% |
2011 | 0.408 | 0.487 | 0.339 | 0.369 | 0.465 | 0.462 | 0.365 | 31.71% | 15.31% | 52.98% |
2012 | 0.401 | 0.475 | 0.337 | 0.365 | 0.454 | 0.450 | 0.363 | 31.76% | 14.00% | 54.23% |
2013 | 0.400 | 0.468 | 0.341 | 0.366 | 0.445 | 0.441 | 0.367 | 31.98% | 13.70% | 54.33% |
2014 | 0.413 | 0.465 | 0.379 | 0.372 | 0.455 | 0.438 | 0.388 | 32.38% | 9.99% | 57.62% |
2015 | 0.399 | 0.466 | 0.338 | 0.372 | 0.438 | 0.438 | 0.369 | 32.12% | 12.44% | 55.44% |
2016 | 0.402 | 0.472 | 0.344 | 0.369 | 0.442 | 0.439 | 0.372 | 32.06% | 13.17% | 54.78% |
2017 | 0.425 | 0.469 | 0.409 | 0.369 | 0.463 | 0.435 | 0.410 | 32.56% | 17.75% | 49.69% |
2018 | 0.401 | 0.472 | 0.344 | 0.368 | 0.436 | 0.436 | 0.373 | 32.11% | 14.10% | 53.80% |
2019 | 0.427 | 0.474 | 0.414 | 0.370 | 0.464 | 0.441 | 0.407 | 32.68% | 15.74% | 51.59% |
2020 | 0.422 | 0.475 | 0.393 | 0.368 | 0.460 | 0.439 | 0.404 | 32.32% | 20.59% | 47.09% |
Fig. 1
Trends in the evolution of overall intra-regional disparities in nursing human resources
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Fig. 2
Trends in the evolution of disparities in nursing human resources
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As can be seen from the inter-regional differences, the inter-regional differences in human resources for care in the Yangtze River Economic Belt show a clear evolutionary trend in the period examined from 2010 to 2020, as shown in Fig. 3. Overall, the inter-regional disparities show a decreasing trend in this period, but the differences between regions are characterized by statistically significant differences. In particular, the gap between upstream - midstream regions is smaller, while the gap between upstream-downstream regions is always larger. Specifically, the gap between upstream and downstream experienced a fluctuating process of first decline, then rise, and then decline, reflecting the dynamic adjustment and policy impact of the allocation of nursing human resources between the regions. Meanwhile, the gap between upstream - midstream shows a continuous downward trend, which may be related to the more balanced economic development and human resource policies in the region. In contrast, the change in the gap between the middle - downstream is more unstable, with an overall fluctuating upward trend, which may be related to the region’s uneven internal development and changes in the external economic environment. Its evolution is closely related to the level of development of each region. The upstream-midstream regions are at a higher level of economic development, and the level of nursing human resource allocation has also been maintained at a higher level. The upstream and midstream regions, on the other hand, are at a lower level of development relative to the downstream regions, resulting in a large gap in the level of nursing human resource allocation. However, with the gradual strengthening of the government’s supportive policies for the upstream regions, nursing human resources in the upstream regions have developed rapidly. Nevertheless, although policy support has led to the development of human resources for care in the upstream areas, the overall level is still lower than that in the middle and downstream areas, and more policy optimisation and resource inputs are needed to achieve balanced development.
Fig. 3
Inter-regional disparities and evolutionary trends
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Fig. 4
Sources and contributions of evolutionary trends inter-regional disparities
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In terms of the contribution rate of regional differences, Between 2010 and 2020, the inter-regional gap in human resources for care in the Yangtze River Economic Belt is mainly affected by two factors: hypervariable density and inter-regional disparity. In terms of the overall evolution process, the contribution rate of hypervariable density and the contribution rate of inter-regional disparity show different trends over the period examined. Specifically, the contribution rate of hypervariable density shows a fluctuating downward trend throughout the examination period. The contribution rate of hypervariable density was as high as 51.66% at the beginning, and after experiencing fluctuations, it dropped to 47.09% by 2020. This downward trend reflects the dynamic adjustment and optimisation of the Yangtze River Economic Belt in terms of the allocation of nursing human resources, and the impact of hypervariable density on the interregional disparity of nursing human resources is gradually weakening with the gradual improvement of policy support and resource allocation. Contrary to the trend in the contribution rate of hypervariable density, the contribution rate of intraregional disparities rebounded to 20.59 per cent after dropping from 16.80 per cent to 9.99 per cent, while the contribution rate of interregional disparities was relatively stable, peaking at 32.68 per cent. In terms of the main source of the contribution rate, hypervariable density has been the main source of the interregional gap in nursing human resources. This suggests that hypervariable density plays a decisive role in the allocation of nursing human resources in the Yangtze River Economic Belt, while the contribution rate of interregional disparity is relatively small. This may be related to a variety of factors, such as the geographic characteristics of the Yangtze River Economic Belt, the level of economic development, and policy support.
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Convergence analysis
σ convergence analysis
The evolution trend of the coefficient of variation of nursing human resources in the Yangtze River Economic Belt in general and in the upper, middle, and downstream regions is shown in Table 3. From the overall level: nursing human resources have obvious σ convergence in the examination period of 2010–2020, and there is a phased σ convergence; from 2010 to 2013, the σ value continued to decline from 0.791 to 0.720, indicating that there is σ convergence in nursing human resources at this stage; after a small rebound in 2014, it is in a 2015–2017 fluctuating downward trend, and finally rebounded again in 2019, falling to 0.733 in 2020. from regional fluctuating trend; the upstream region declined from 1.448 in 2010 to 1.366 in 2020, with an average annual rate of decline of 5.66%. The middle reaches decreased from 0.961 in 2010 to 0.762 in 2020, with an average annual rate of 20.71%; the downstream region decreased from 1.001 in 2010 to 0.868 in 2020, with an average annual rate of 13.29%. This indicates that the regions of the Yangtze River Economic Belt exhibit different degrees of convergence during the study period, with the midstream region showing the most statistically significant average annual rate of decline, suggesting that the region has achieved a faster rate of convergence in the development of human resources for nursing, while the upstream and downstream regions have relatively slower rates of convergence.
Table 3
Convergence coefficients of human resource development in CKE care
Provinces | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Total | 0.791 | 0.773 | 0.748 | 0.720 | 0.749 | 0.717 | 0.723 | 0.712 | 0.726 | 0.744 | 0.733 |
Upper reaches | 1.448 | 1.440 | 1.416 | 1.3730 | 1.366 | 1.366 | 1.380 | 1.356 | 1.3690 | 1.373 | 1.366 |
Middle reaches | 0.961 | 0.782 | 0.769 | 0.7820 | 0.835 | 0.830 | 0.817 | 0.818 | 0.807 | 0.758 | 0.762 |
Lower reaches | 1.001 | 0.969 | 0.940 | 0.924 | 0.970 | 0.893 | 0.884 | 0.921 | 0.899 | 0.924 | 0.868 |
Sichuan | 1.389 | 1.389 | 1.351 | 1.330 | 1.312 | 1.306 | 1.347 | 1.327 | 1.329 | 1.365 | 1.368 |
Guizhou | 0.767 | 0.711 | 0.632 | 0.591 | 0.565 | 0.552 | 0.535 | 0.524 | 0.523 | 0.511 | 0.503 |
Chongqing | 0.339 | 0.332 | 0.331 | 0.315 | 0.314 | 0.271 | 0.268 | 0.224 | 0.217 | 0.129 | 0.124 |
Jiangxi | 0.571 | 0.552 | 0.565 | 0.558 | 0.569 | 0.573 | 0.573 | 0.595 | 0.608 | 0.615 | 0.630 |
Hunan | 0.665 | 0.672 | 0.655 | 0.661 | 0.669 | 0.647 | 0.632 | 0.620 | 0.615 | 0.521 | 0.570 |
Hubei | 0.915 | 0.928 | 0.916 | 0.935 | 1.004 | 1.006 | 0.999 | 1.020 | 1.001 | 1.011 | 0.991 |
Jiangsu | 0.683 | 0.712 | 0.624 | 0.450 | 0.631 | 0.441 | 0.458 | 0.417 | 0.451 | 0.585 | 0.499 |
Anhui | 0.723 | 0.614 | 0.624 | 0.619 | 0.624 | 0.624 | 0.655 | 0.596 | 0.686 | 0.732 | 0.684 |
Shanghai | 0.201 | 0.201 | 0.202 | 0.196 | 0.186 | 0.174 | 0.169 | 0.154 | 0.089 | 0.036 | 0.021 |
Zhejiang | 0.615 | 0.604 | 0.620 | 0.613 | 0.620 | 0.589 | 0.586 | 0.599 | 0.598 | 0.612 | 0.620 |
Absolute β-convergence
In this paper, the spatial econometric model is used to deeply explore the absolute β convergence of the level of nursing human resources in nine provinces and municipalities directly under the central government in the Yangtze River Economic Belt. As can be seen in Table 4, the β convergence coefficients of the Yangtze River Economic Belt as a whole and the upstream, midstream, and downstream regions are all positive, 0.0704, 0.052, 0.057, and 0.050, respectively, and all of them have passed the significance test at the 1% level. The results show that within the Yangtze River Economic Belt, nursing human resource development does not show a trend of absolute β-convergence, but tends to diverge. It indicates that there are differences in the speed of nursing human resource development among provinces and municipalities directly under the central government in the Yangtze River Economic Belt, and the differences may further expand to a certain extent. In addition, the traditional absolute convergence model does not fully consider the spatial effect in the analysis, while this study found that the spatial autocorrelation coefficient ρ of the upstream region of the Yangtze River Economic Belt is greater than 0 and statistically significant at the 1% level, which reveals that geographic units with similar eigenvalues among the provinces in the upstream region tend to be clustered together, i.e., presenting a “high high agglomeration” and “low low agglomeration”. The phenomenon of “high and high agglomeration” and “low and low agglomeration”. In contrast, the spatial autocorrelation coefficient ρ of the midstream region is less than 0 and also passes the significance test at the 1% level, indicating that geographic units with opposite eigenvalues at the level of nursing human resources in the midstream region tend to be adjacent to each other, i.e., they are characterized by spatially dispersed or alternating patterns, i.e., “high-low agglomeration” “low-high agglomeration” phenomenon. This spatial distribution feature reflects the fact that the midstream region is the main reason for the imbalance in the development of nursing human resources.
Table 4
Absolute β convergence results for nursing human resources in the Yangtze River Economic Belt
Synthesis | Upper reaches | Middle reaches | Lower reaches | |||||
|---|---|---|---|---|---|---|---|---|
OLS | SDM | OLS | SDM | OLS | SDM | OLS | SDM | |
β | 0.0701*** | 0.0704*** | 0.094*** | 0.052*** | 0.057*** | 0.057*** | 0.051*** | 0.050*** |
(0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | |
Ρ | -0.292 | 0.172*** | -0.120* | -0.153 | ||||
(0.51) | (0.00) | (0.08) | (0.83) | |||||
Rate of convergence (%) | 0.007 | 0.111 | 0.009 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 |
R2 | 0.246 | 0.334 | 0.423 | 0.496 | ||||
observed value | 290 | 80 | 100 | 110 | ||||
Conditional β-convergence
Further examine the conditional β convergence test for the convergence characteristics of nursing human resources by introducing factors such as the level of economic development, the standard of living of the population, medical and health resources, and the level of education.
As can be seen from Table 5, based on the absolute β convergence analysis, adding a series of spatial effect influencing factors, except for the upstream area which did not pass the significance test, the β convergence coefficients of the whole and the upstream, midstream and downstream were − 2.971, 0.104, -2.614 and − 1.101 respectively, and passed the significance test of 1%. It indicates that the allocation of nursing human resources in these regions is showing a trend of convergence under the consideration of spatial effects and control variables. Although the coefficient of β-convergence for the upstream region did not pass the significance test, its negative value still implies that there may be a certain degree of decentralization tendency in the allocation of nursing human resources in this region. This phenomenon may be closely related to a variety of factors such as the geographic location of the upstream region, the level of economic development, and policy support. From the point of view of convergence speed and half-life cycle, compared with the absolute β-convergence model, the overall, midstream, and downstream show convergence characteristics, indicating that spatial effects and control variables have an obvious role in promoting the convergence of nursing human resources in the overall, midstream, and downstream. The speed of convergence finally showed the overall (12.5%) > midstream (11.8%) > downstream (6.7%) under the condition of considering spatial effects and control variables. The results indicate that spatial effects and control variables play an important role in promoting the convergence of nursing human resource allocation, and the degree of their role is heterogeneous across regions.
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In addition, in terms of control variables, the coefficients and significance levels of the influencing factors in the Yangtze River Economic Belt as a whole and in the upstream, middle, and downstream regional levels have different moderating effects. The regional economic level is significantly and positively correlated with the level of nursing human resources in the overall and upstream regions, emphasising the supportive role of economic development in the growth of nursing human resources. However, this positive correlation is not significant in the midstream and downstream regions, reflecting the variability in the impact of economic development on human resources for nursing across regions. The standard of living of residents shows a significant effect in the upstream and downstream regions, while it is not statistically significant in other regions. It indicates that in the upstream and downstream regions, higher living standards of residents create an attraction and retention effect on nursing workers, thus contributing to the level of nursing human resources. However, in other regions, the effect of residents’ living standards on nursing human resources may be influenced by, for example, working environment, career development opportunities, or policy support. The level of educational development was statistically significantly and negatively correlated with the level of nursing human resources in the overall and downstream regions, which may indicate that the current structure and mode of educational expenditure may not be effectively targeting the training needs of nursing human resources, or that there is a disconnect between educational inputs and the actual needs of nursing human resources. It suggests the need for further research on how to optimize the allocation of educational resources to more effectively support the development of nursing human resources. Health resources were statistically significantly and positively associated with the level of nursing human resources at both the overall and regional levels, which may reflect the direct effect of the size of the health facility on the demand for nursing human resources, i.e., an increase in the number of beds tends to require more nursing staff to provide patient care services. This finding emphasizes the significance of health resources on the level of nursing human resources. The speed of convergence indicates that the level of nursing human resources in the Yangtze River Economic Belt as a whole and the upstream regions shows a clear conditional β-convergence trend, i.e., the growth rate of regions with lower levels of nursing human resources is faster over time, which helps to narrow the gap in nursing human resources among different regions when the effects of control variables are taken into account.
Table 5
Convergence results of β condition of nursing human resources in the Yangtze River Economic Belt
Synthesis | Upper reaches | Middle reaches | Lower reaches | |||||
|---|---|---|---|---|---|---|---|---|
OLS | SDM | OLS | SDM | OLS | SDM | OLS | SDM | |
β | -0.423*** | -2.971*** | 0.049*** | 0.104 | 0.001 | -2.674*** | -1.313*** | -1.100*** |
(0.00) | (0.00) | (0.00) | (0.33) | (0.95) | (0.00) | (0.00) | (0.00) | |
Gross regional product/million yuan | 4.91 | 0.00003*** | -3.97 | 1.25 | 2.452 | 0.00005*** | 2.267 | 0.00014*** |
(0.06) | (0.00) | (0.814) | (0.42) | (0.73) | (0.00) | (0.81) | (0.00) | |
Average wage of employees/¥ | 0.003 | 0.130 | -0.008*** | -0.180** | -0.006 | -0.142 | 0.019* | 0.049* |
(0.393) | (0.153) | (0.00) | (0.002) | (0.09) | (0.12) | (0.02) | (0.03) | |
Number of beds in hospitals, health centers/beds | 0.583*** | 0.124*** | 0.268*** | 0.022** | 0.371*** | 0.286* | 0.102*** | 0.347*** |
(0.00) | (0.00) | (0.00) | (0.003) | (0.00) | (0.04) | (0.00) | (0.00) | |
Expenditure on education level | 1186.002 | 978.634 | 1909.041 | 1189.61 | 728.02 | -8241.08* | -300.400 | -1467.014 |
(0.466) | (0.74) | (0.06) | (0.37) | (0.14) | (0.02) | (0.94) | (0.82) | |
ρ | -0.251 | 0.219** | -0.166* | -0.081 | ||||
0.57 | 0.009 | 0.016 | 0.25 | |||||
R2 | 0.468 | 0.435 | 0.234 | 0.523 | ||||
observed value | 290 | 80 | 100 | 110 | ||||
Rate of convergence (%) | -0.032 | -0.125 | 0.005 | 0.010 | 0.000 | -0.118 | -0.076 | -0.067 |
Discussion
This study adopts the 2010–2020 nursing human resources data of 114 prefecture-level cities in the Yangtze River Economic Belt, and empirically researches the spatial differences in nursing human resources in the Yangtze River Economic Belt and the mechanism of convergence characteristics through the Dagum’s Gini coefficient and its decomposition method and the convergence model, and obtains the following conclusions:
The total amount of human resources cared for in the Yangtze River Economic Belt has increased, but the problem of unbalanced regional development still exists
The total human resources for care in the Yangtze River Economic Belt increased from 812,400 in 2010 to 2,030,200 in 2020, and this growth trend not only reflects the steady improvement in the level of nursing services, but also signifies that nursing has made remarkable progress in the region. However, despite the increase in overall human resources, the problem of unbalanced regional development remains prominent, echoing the findings of scholars such as Jingxian Wu and Yongmei Yang [34]. Notably, this growth is consistent with the expected goal of total nursing human resources reaching 4.45 million, as proposed in the national National Nursing Career Development Plan (2016–2020) [35]. The growth in nursing human resources is on the one hand due to the strong support of national policies, which have successfully attracted a large number of nursing professionals through the implementation of measures such as employment incentives, improvement of remuneration, optimization of the working environment, and enhancement of the quality of nursing education [36]; on the other hand, the densely populated Yangtze River Economic Belt, with the increase in the aging and diversified social needs and the consequent rise in the demand for nursing services, which has further driven the the level of nursing human resource allocation [37]. Given the imbalance in the distribution of nursing human resources in the Yangtze River Economic Belt, strategic adjustment at the regional level is particularly important. It is recommended to organize medical institutions in developed regions to establish counterpart support relationships with medical institutions in weak regions, so as to improve the quality of nursing services and management level in weak regions through expert dispatch and technical guidance. In addition, targeted training programs should be developed to attract and cultivate nursing talents in response to the actual needs of weak regions, and to ensure that they can be directed to serve these regions after graduation [38]. The government’s investment in nursing education and training also needs to be increased in order to improve the treatment of nursing personnel and attract more outstanding talents to join the nursing profession. Constructing a more complete nursing talent training system is crucial to solving the problem of uneven distribution of nursing human resources within the Yangtze River Economic Belt.
Regional differences in nursing resources in the Yangtze River Economic Belt are rising year by year, and intra-regional differences are greater than inter-regional differences
The overall Gini coefficient for the level of nursing human resources in the Yangtze River Economic Belt rises from 0.409 in 2010 to 0.422 in 2020, with the contribution of interregional differences stabilizing between [31.71%, 32.68%] and that of intraregional differences fluctuating between 9.99 and 20.59%, a phenomenon that reveals the relative importance of intraregional differences. Such differences may be closely related to differences in the age structure and disease spectrum of the population in each region, which in turn affects the pattern of demand for nursing services. Specifically, downstream regions such as Shanghai have a high level of economic development and urbanization (88.10% urbanization rate), and show a clear trend of aging, resulting in a particularly urgent need for geriatric care and chronic disease management [39‐41]. In contrast, the midstream region is in the rapid development stage of industrialization and urbanization, with a relatively young population structure and abundant labor resources, and its nursing needs are mainly focused on basic medical services and occupational health protection [42]. As for the upstream area, due to its mountainous natural conditions and relatively lagging level of economic development, it has become a major labor exporting area, and the large-scale outward migration of young and middle-aged laborers has led to the problems of aging local population and left-behind children, and its health needs are mainly focused on the prevention and control of infectious diseases, maternal and child health care, and basic medical services [43, 44]. In order to promote the balanced development of nursing human resources in the Yangtze River Economic Belt, it is necessary to formulate a reasonable development plan based on the characteristics of each region and implement a new development strategy of differentiation. For regions with abundant nursing human resources, resource allocation should be optimized and manpower structure should be adjusted to improve the structural imbalance [45]. For regions with insufficient resource allocation, the level of nursing human resource allocation can be gradually improved through measures such as attracting talents through policies and setting competitive welfare benefits [46].
The human resources for nursing in the Yangtze River Economic Belt have spatial convergence, with the speed distributed in a “downstream -middlestream -downstream” step
Through the analysis of the convergence results, it is found that the convergence coefficient shows a decreasing trend year by year, indicating the existence of σ-convergence phenomenon. Specifically, from the perspective of absolute β-convergence, the upper, middle and lower reaches of the Yangtze River Economic Belt do not show obvious absolute β-convergence characteristics. However, when the influential factors such as the level of economic development and the standard of living of residents are taken into account, i.e., the conditional β-convergence analysis shows that the convergence rates of the overall, midstream and downstream regions are decreasing in turn, which reflects that the development of nursing human resources in the regions is gradually becoming balanced, i.e., the differences in nursing human resources between the regions are being gradually reduced, after controlling for these factors. The reason for this is that the lower reaches of the Yangtze River, such as the provinces and cities of Shanghai, Jiangsu and Zhejiang, have a high degree of economic maturity, the nursing human resources market is close to saturation, and the growth potential is limited, which leads to a relatively slow rate of narrowing of the gap. In contrast, the middle reaches of the Yangtze River benefit from rapid economic growth, strong demand for nursing human resources and huge market potential, and therefore show a faster growth rate, which contributes to the balanced development of nursing human resources in the region. As for the upstream region, its relatively low level of economic development, coupled with its remote location and inconvenient transportation, has led to limited coverage and accessibility of nursing services, which to a certain extent restricts the cultivation and development of nursing human resources, making the region’s performance in terms of spatial and conditional convergence both insignificant.
Limitations
In this paper, the regional differences and convergence trends of nursing human resources in the Yangtze River Economic Belt are investigated by using the Dagumu Gini coefficient and its decomposition method and the convergence model, which, compared with similar studies, can make up for the inadequacy of the decomposition of the causes of the regional differences to a certain extent, but there are certain limitations as well. On the one hand, due to the limitation of data availability, the study fails to comprehensively cover all potential influencing factors, such as region-specific health needs and health policy details. On the other hand, the study may not have adequately considered the short-term effects of policy changes on the distribution of nursing human resources, as well as the effects of intra-regional micro-dynamics such as inter-city labor mobility. Therefore, future studies should consider including a wider range of data sources and using more sophisticated statistical methods to improve the depth and accuracy of the analysis, and should update the findings as new data become available. Therefore, further improvements will be made in future studies to address the shortcomings.
Conclusions
This study adopts the 2010–2020 nursing human resources data of 114 prefecture-level cities in the Yangtze River Economic Belt, and empirically researches the spatial differences in nursing human resources in the Yangtze River Economic Belt and the mechanism of convergence characteristics through the Dagum Gini coefficient and its decomposition method and the convergence model. The conclusions of the study are as follows: first, the total amount of nursing human resources in the Yangtze River Economic Belt has increased, but regional development imbalance still exists. It indicates that although the nursing human resources in the Yangtze River Economic Belt as a whole have been expanded, the distribution of nursing talents among different cities and regions has not reached the ideal equilibrium. Second, regional differences in nursing resources in the Yangtze River Economic Belt have been rising year by year, with intra-regional differences greater than inter-regional differences. It means that over time, nursing human resources in some cities within the economic belt become more concentrated, while other regions face the challenge of brain drain or slow growth. Third, nursing human resources in the Yangtze River Economic Belt are spatially convergent, with a “downstream-middlestream-upstream” step distribution. That is to say, as time goes by, the original nursing resource-poor regions begin to catch up with the resource-rich regions, so that the distribution of nursing human resources in the region tends to be more balanced. In particular, the downstream regions with more developed economies converge faster than the upstream regions with less developed economies, and it is found that external factors such as the level of economic development and education influence the speed of convergence between different regions. Therefore, in order to promote the balanced development of nursing human resources in the Yangtze River Economic Belt, it is necessary to adopt a more refined strategy in policy formulation and resource allocation, which not only focuses on increasing the total supply of nursing talents, but also devotes itself to narrowing the gap of nursing human resources between regions and even within the same region, especially in the economically less-developed upstream region to strengthen the cultivation and introduction of nursing talents, so as to accelerate the balanced distribution of nursing The balanced distribution of nursing human resources will ultimately realize the high quality and fair accessibility of nursing human resources in the Yangtze River Economic Belt.
Acknowledgements
The data used in this paper come from the 2011-2021 China Urban Statistical Yearbook, provincial statistical yearbooks, and national economic and social development statistical bulletins. The authors would like to thank all the members for their time and efforts in the various projects.
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Competing interests
The authors declare no competing interests.
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