Analysis of Resource Allocation Fairness of Registered Nurses in the Guangdong Province

Analysis of Resource Allocation Fairness of Registered Nurses in the Guangdong Province | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Resource Allocation Fairness of Registered Nurses in the Guangdong Province Wei Meng, Xiaomai Wu, Jingwen He, Qinglan Kuang, Xianglan Peng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3795189/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aimed to assess the fairness of resource allocation of registered nurses in the Guangdong Province, based on the Healthy China strategy. It aimed to identify the issues with resource allocation fairness and provide optimisation suggestions. Methods To assess the allocation fairness of registered nurses, the study used the Gini coefficient, health resource density index, Lorenz curve, and index of dissimilarity. Additionally, the study employed three methods to calculate the Gini coefficient to analyse equity among registered nurses. Results In 2021, the allocation of registered nurses in hospitals accounted for 71.22% of registered nurses in the province, that of registered nurses in general hospitals accounted for 72.73% of the hospitals, and that of registered nurses in tertiary medical institutions accounted for 62.28% of the medical institutions at all levels. In terms of health resource allocation density, the demographic density of registered nurses in Guangzhou and Zhuhai was greater than 1; the difference index was 0.44 and 0.43 by geography and population, respectively. Calculated by population, three methods were used to calculate the Gini coefficient, taking the mean value to be 0.31; according to the geographical distribution, the average of the three calculation methods was taken, and the Gini index of registered nurses in the functional districts was 0.39. Conclusion When considering the geographical allocation, it is evident that there are disparities in the fair distribution of registered nurses in Guangdong. Specifically, the allocation of registered nurses in the west wing of the coastal economic belt and the ecological development zone (mountain area) of northern Guangdong is insufficient, as indicated by the Gini coefficient of different functional zones. This study recommends improving regional coordinated development to enhance the fairness of registered nurses’ allocation in the Guangdong province. registered nurse Guangdong province medical institution fair allocation Figures Figure 1 Figure 2 INTRODUCTION Nursing human resources constitute an important part of human resources in the healthcare field ( 1 ). They serve as a basis for ensuring the quality of care, human health, and quality of life. They have a direct impact on the development of healthcare, making them an integral part of health and healthcare enterprises. Subsequently, in response to the ageing population in China, there should be a balanced allocation of health resources in the country to ensure that everyone can enjoy high-quality medical resources ( 2 ); this would help in comprehensively promoting the construction of a healthy China. On 14 May 2023, the General Office of the State Council issued Opinions on Promoting the High-Quality Development of Public Hospitals. These opinions clearly state that measures such as reforming the personnel management system, implementing a post-management system, and increasing the number of nurses should be taken to promote the high-quality development of public hospitals. At present, the core problem of nursing human resource allocation lies in ensuring an appropriate number of personnel and structure administration ( 3 ). To resolve the abovementioned issues in resource allocation, the 14th Five-Year Plan of the People’s Republic of China (PRC) has been passed to deepen the country’s medical and healthcare reform. Within this outline, the ‘National Economic and Social Development and Long-term Target Outline for 2035’ aims to increase the number of registered nurses per 1,000 permanent residents in China to 3.8 by 2025. Moreover, the ‘Healthy China 2030’ planning outline aims to establish a 15-minute basic medical and health service circuit by 2030, striving for 4.7 registered nurses per 1,000 permanent residents. The ultimate goal is to ensure that everyone has equal access to basic medical and health services and that high-quality nursing resources are distributed evenly. A fair and effective distribution of health resources is essential for the sustainable development of health services ( 4 ). Nurses play an essential role in medical institutions and their importance cannot be underestimated. China particularly prioritises the development of nurses and strives to achieve a fair distribution of registered nurses. It is ensured that all nurses can provide care based on the same standards and are treated equally under similar conditions. Various strategies have been adopted to improve the professional quality and technical skills of nurses, promote the stability and growth of nursing teams, and ensure the smooth implementation of nursing work. However, there is little research on the regional differences and inefficiency of health resource allocation and health service utilization in China ( 2 ). Nursing has been gradually developed in China, but there are still some issues. First, compared with the demand for medical services, the number of registered nurses in China is insufficient ( 5 – 7 ) ( 8 ), and the allocation of nursing human resources is unreasonable. In fact, the number of registered nurses in China accounts for only about 15% of the total number of health professionals, far below the average of 40% in developed countries. Second, the professional quality of registered nurses is not high. With China’s economic development and social progress, people’s demand for medical services is increasing. However, at present, the quality of registered nurses in China cannot meet people’s growing health needs. According to a recent survey( 5 – 8 ), the proportion of nurses with a university degree or higher in China is only about 20%, which lags far behind that in developed countries. Third, the development of nursing dispensation in China is uneven, and nursing products are inconsistent, with significant differences between regions and urban and rural areas. This study explores several areas of research to address the problems of inadequate allocation, imbalance, and low quality of nursing staff in the Guangdong province. This research includes assessing the current nursing workforce. A comprehensive assessment of the current nursing workforce in Guangdong province can provide insight into the number of nurses, their qualifications, skills, and areas of dispensation. Various calculation methods have been used to assess the knowledge of the nursing workforce; these methods provide a valuable understanding of the nursing staffing needs and resource allocation in healthcare facilities. By using these methods, healthcare institutions can optimise their staffing levels, improve patient outcomes, and increase the overall efficiency and effectiveness of care delivery. The aim of the analysis was to evaluate the fairness of resource allocation for registered nurses in the Guangdong Province of China. The research question was be: “To what extent is resource allocation for registered nurses in the Guangdong Province fair and equitable? Therefore, the findings of this study can help identify gaps in nursing staff allocation and determine the extent of imbalance in different specialties, ultimately improving the quality and safety of nursing care in the region. METHODS Data source The data on health human resources (registered nurses) in medical and healthcare institutions in this study were obtained from the Guangdong Province Health and Family Planning Statistical Yearbook. Regional per capita gross domestic product (GDP) and resident population data were collected from the Guangdong Province Statistical Yearbook(2021). The data on geographic areas were derived from the administrative division information provided by the Ministry of Civil Affairs of Guangdong ( 9 ). Setting According to the classification provided in the Statistical Yearbook of the Guangdong province (Fig. 1 A, map of Guangdong province), Guangdong is divided into four regions (Fig. 1 B): the Pearl River Delta zone, including Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen, and Zhaoqing; east wing of the coastal economic belt (east wing), including Shantou, Shanwei, Jieyang, and Chaozhou; west wing of the coastal economic belt (west wing), including Zhanjiang, Maoming, and Yangjiang; and the northern ecological development zone of Guangdong (mountainous area), including Shaoguan, Heyuan, Meizhou, Qingyuan, and Yunfu ( 9 ). Measures Gini coefficient The Gini coefficient, G, is a measure of income inequality. According to its criterion, when the Gini coefficient is less than 0.2, the income of residents is considered to be evenly distributed. When it falls between 0.2 and 0.3, the income distribution is considered to be moderately equal. When it ranges from 0.3 to 0.4, the income distribution is considered to be reasonably equal. When it falls between 0.4 and 0.5, the income gap is considered to be large. When it is greater than 0.5, the income gap is considered to be very wide ( 10 , 11 ). In this paper, the following formulas were used to calculate the Gini coefficient ( 12 – 14 ): $$\text{G}=1-\frac{2}{3\text{n}}[1+2({\text{I}}_{2}+{\text{I}}_{4}+\cdots +{\text{I}}_{\text{n}-2})+4({\text{I}}_{1}+{\text{I}}_{3}+\cdots +{\text{I}}_{\text{n}-1}\left)\right]$$ The fitting curve method was used to solve the problem, while the least square method was used for estimation. The steps used are as follows: Step 1: Draw points based on the collected data, observe the trend of the discrete points, and determine the type and expression of the fitting function. Step 2: Calculate the parameters: \({\text{a}}_{0},{\text{a}}_{1},\cdots ,{\text{a}}_{\text{n}}\) Step 3: Use the formula, \(\text{G}=\frac{{\text{S}}_{\text{A}}}{{\text{S}}_{\text{A}}+{\text{S}}_{\text{B}}}=1-2{\text{S}}_{\text{R}}=1-2{\int }_{0}^{1}{\phi }\left(\text{p}\right)\text{d}\text{p}\) . The fitting function selects the algebraic polynomials: \({\phi }\left(\text{p}\right)={\text{a}}_{0}+{\text{a}}_{1}\text{p}+{\text{a}}_{2}{\text{p}}^{2}+\cdots +{\text{a}}_{\text{m}}{\text{p}}^{\text{m}}\) The equations related to the corresponding fitting function selection of the algebraic polynomial are as follows: $$\left[\begin{array}{cccc}\text{n}+1& \sum {\text{p}}_{\text{i}}& \cdots & \sum {\text{p}}_{\text{i}}^{\text{m}}\\ \sum {\text{p}}_{\text{i}}& \sum {\text{p}}_{\text{i}}^{2}& \cdots & \sum {\text{p}}_{\text{i}}^{\text{m}+1}\\ \cdots & \cdots & \cdots & \cdots \\ \sum {\text{p}}_{\text{i}}^{\text{m}}& \sum {\text{p}}_{\text{i}}^{\text{m}+1}& \cdots & \sum {\text{p}}_{\text{i}}^{2\text{m}}\end{array}\right]\left[\begin{array}{c}{\text{a}}_{0}\\ {\text{a}}_{1}\\ ⋮\\ {\text{a}}_{\text{m}}\end{array}\right]=\left[\begin{array}{c}\sum {\text{I}}_{\text{i}}\\ \sum {\text{p}}_{\text{i}}{\text{I}}_{\text{i}}\\ \sum {\text{p}}_{\text{i}}^{2}{\text{I}}_{\text{i}}\\ ⋮\\ \sum {\text{p}}_{\text{i}}^{\text{m}}{\text{I}}_{\text{i}}\end{array}\right]$$ Solve the parameters \({\text{a}}_{0},{\text{a}}_{1},\cdots ,{\text{a}}_{\text{n}}\) and calculate the \({\phi }\left(\text{p}\right)\) (Gini coefficient). Health resource density index(HRAD) According to past literature ( 11 , 15 ), the health resource aggregation degree (HRAD) can be used to evaluate the fair allocation of medical and health resources. The calculation formula for the concentration degree of health resources, which includes population and geographical area, objectively reflects the balanced degree of registered nurses’ resource allocation among different groups in the region ( 16 ). The population agglomeration degree (PAD) refers to the percentage of population concentrated in a specific area, relative to the total land area of that region. It is calculated using the following formula: $$\text{H}\text{R}\text{A}{\text{D}}_{\text{i}}=\frac{（\text{H}{\text{D}}_{\text{i}}/\text{H}{\text{R}}_{\text{n}}）\times 100\text{%}}{（{\text{A}}_{\text{i}}/{\text{A}}_{\text{n}})\times 100\text{%}}=\frac{\text{H}{\text{R}}_{\text{i}}/{\text{A}}_{\text{i}}}{\text{H}{\text{R}}_{\text{n}}/{\text{A}}_{\text{n}}}$$ \(\text{H}\text{R}\text{A}{\text{D}}_{\text{i}}\) represents the concentration of health resources in a certain area I, \(\text{H}{\text{R}}_{\text{i}}\) indicates the number of registered nurses, \({\text{A}}_{\text{i}}\) represents the land area, \({\text{A}}_{\text{n}}\) represents the land area in the upper level area, and \(\text{H}{\text{R}}_{\text{n}}\) indicates the number of registered nurses in the upper level area. The population agglomeration degree (PAD) indicates the proportion of the population gathered in a certain area, which accounts for 1% of the land area of the upper level area. The calculation formula is as follows: \(\text{P}\text{A}{\text{D}}_{\text{i}}=\frac{{(\text{P}}_{\text{i}}/{\text{P}}_{\text{n}})\times 100\text{%}}{({\text{A}}_{\text{i}}/{\text{A}}_{\text{n}})\times 100\text{%}}=\frac{{\text{p}}_{\text{i}}/{\text{A}}_{\text{i}}}{{\text{P}}_{\text{n}}/{\text{A}}_{\text{n}}}\) \(\text{P}\text{A}{\text{D}}_{\text{i}}\) represents the population density of an area I, \({\text{p}}_{\text{i}}\) represents the population, \({\text{A}}_{\text{i}}\) represents the land area, \({\text{A}}_{\text{n}}\) represents the land area of the upper level area, and \({\text{p}}_{\text{n}}\) represents the total population of the upper level area. The evaluation criteria of the abovementioned calculations are as follows: ( 1 ) \(\text{H}\text{R}\text{A}{\text{D}}_{\text{i}}>1\) : this shows that the distribution of registered nurses by geographical area is fair; ( 2 ) \(\frac{\text{H}\text{R}\text{A}{\text{D}}_{\text{i}}}{\text{P}\text{A}{\text{D}}_{\text{i}}}\sim1:\) This shows that the allocation of registered nurses is fair, which can basically meet the needs of the local population and the health service is accessible. When \(\frac{\text{H}\text{R}\text{A}{\text{D}}_{\text{i}}}{\text{P}\text{A}{\text{D}}_{\text{i}}}>1,\) it shows that registered nurses are in surplus compared to the population size; meanwhile, when \(\frac{\text{H}\text{R}\text{A}{\text{D}}_{\text{i}}}{\text{P}\text{A}{\text{D}}_{\text{i}}}<1,\) this shows that the registered nurses are unable to meet the population demand ( 17 ). Lorenz curve The Lorenz curve is used to show the fairness of income distribution of residents in a country or region; it has also been implemented to identify the fairness of health resource allocation ( 11 , 14 ). Drawing on the Lorenz curve, the abscissa refers to the cumulative ratio of permanent population or regional area, and the ordinate represents the cumulative percentage of registered nurses. The fairness of assigned registered nurses is evaluated by the bending degree of the Lorenz curve, and the more the curve bends, the worse it is in terms of fairness ( 18 ). Index of dissimilarity The Index of dissimilarity ( 19 ) (ID) uses the proportion of registered nurses and the corresponding proportion of population or geographical area to evaluate the degree of difference in the allocation of medical and health resources, thus reflecting the fairness of the allocation of medical and health resources in various cities. It is calculated according to the formula: \(\text{I}\text{D}=\frac{1}{2}\sum \left|{R}_{An}-{R}_{Ap}\right|,\) in which A is the regional grouping, \({R}_{An}\) is the number of registered nurses in Group A that accounts for the proportion; and \({R}_{Ap}\) is the proportion of the number of permanent residents or geographical area in Group A to the total number of permanent residents or geographical area, respectively. The criterion of difference index is 0 ~ 1, and the closer it is to 0, the better the fairness of health resource allocation. Ethical Equity and Justice. One ethical consideration in resource allocation for registered nurses is ensuring equity and justice. It is important to ensure that resources are distributed fairly among nurses, regardless of factors such as gender, race, or socioeconomic status. This requires considering the needs and capabilities of each nurse and ensuring that they have equal access to necessary resources and opportunities. Patient Safety and Quality of Care. Another ethical consideration is the impact of resource allocation on patient safety and the quality of care. Nurses play a crucial role in providing safe and effective care, and inadequate allocation of resources can compromise patient safety. Ethical decision-making should prioritize the allocation of resources in a way that ensures optimal patient outcomes and upholds the principles of beneficence and non-maleficence. Continuous Evaluation and Improvement. Ethical considerations also involve a commitment to continuous evaluation and improvement of resource allocation processes. Regular assessment and feedback from nurses, patients, and other stakeholders can help identify and address any ethical concerns or shortcomings in resource allocation practices, ensuring ongoing fairness and effectiveness. RESULTS Registered nurses in the region Distribution of nurses The National Nursing Development Plan (2021–2025) stated, on 29 April 2022, that the number of registered nurses per thousand population will reach 3.8 in 2025. According to the year-end statistics of 2021, the number of registered nurses per thousand population in Guangdong province was 31,700, and the resident population was about 126.84 million. The ratio of doctors to nurses and that of beds to nurses were the main factors reflecting the allocation of registered nurses, which determines their work intensity and nursing quality. The findings showed that at present, there are still some differences between the ratio of doctors to nurses and that of beds to households in medical institutions in Guangdong (Table 1). Registered nurse structure Existing research shows that registered nurses have a rich configuration structure; reasonable professional titles and academic qualifications can reduce nursing costs, improve nursing quality, and reduce the incidence of nosocomial infections. Through data analysis, the configuration structure of registered nurses in Guangdong province was analysed to provide a basis for the rational allocation of nursing human resources. In terms of gender, there were around 96.6% female registered nurses; by age, young people aged 34–35 years accounted for about 51% of the registered nurses; in terms of academic background, college graduates accounted for about 41.3% of the nurses; and by professional and technical qualifications, the departmental level accounted for 47.2% of the nurses. Configuration of different medical institutions In 2021, there were more registered nurses in hospitals in the Guangdong province than in primary medical and health institutions, with an average annual growth rate of 2.11% from 2017 to 2021, of which nursing hospitals increased by 111% compared with 2017. Among the types of institutions, the average annual growth rate of registered nurses from 2017 to 2021 was the fastest. According to the hospital level, registered nurses in first-level hospitals showed a downward trend, with a decrease of 14.06% compared with 2017, while the number of registered nurses in third-level hospitals grew the fastest in 2017, with a year-on-year increase of 45.21%. (Table 2 ). Lorenz curve In 2021, Guangdong province had 3.17 registered nurses per 1,000 inhabitants and 2.24 registered nurses per square kilometer (Fig. 2 A Lorenz curve of registered nurse configuration, the population allocation of registered nurses in each city; Fig. 2 B Lorenz curve of registered nurse configuration, registered nurses per square kilometre among cities). Among the cities, Shenzhen, Guangzhou, and Dongguan had a higher number of registered nurses per square kilometre, with 24.38, 12.14 and 11.86 of them, respectively. Medical resource concentration (HRDI) According to the analysis results of the health resource intensity of registered nurses in various cities of Guangdong province (Table 1), it is evident that the density of registered nurses varies greatly according to the geographical allocation level. The density values of Guangzhou, Shenzhen, Zhuhai, Shantou, Foshan, Dongguan, Zhongshan, and Jieyang were all greater than 1, indicating that the registered nurses in these prefecture-level cities were fairly allocated according to geographical area and relatively abundant. Registered nurses in other prefecture-level cities were under-allocated by geographical area and the allocation fairness was poor. Based on past literature ( 20 , 21 ), a deep comparison of the concentration of registered nurses in different regions of Guangdong province was conducted based on the population density. The regions were categorized into densely populated areas (PAD > 2), areas with moderate population (0.5 < PDA < 2), and sparsely populated areas (PAD < 0.5). The findings showed that the distribution of registered nurses in this region was equitable based on population allocation, and the healthcare resources adequately met the nursing needs of the densely populated areas. When HRAD(i)/PAD(i) > 1, this suggests that there is a relative surplus of registered nurses in that particular area compared to the population. When HRAD(i)/PAD(i) < 1, it indicates that the registered nurses in this area may lack sufficient resources. Based on the data, the concentration of registered nurses in densely populated areas of Guangzhou and Zhuhai was higher than 1, suggesting that there may be an excess of registered nurses in these areas. (Table 3 ) Difference index Configuration of registered nurses by functional partition In 2021, Guangdong province was divided into four functional divisions based on population and area allocation. The difference index for population allocation was 0.05, while the difference index for area allocation was 0.37. The calculation of the difference index of the four functional divisions showed that the allocation of registered nurses was fair. According to the concentration of registered nurses, the Pearl River Delta region was 2.06, and the east wing coastal economic belt was 1.30, which is relatively dense. (Table 4 ). Gini coefficient of registered nurses by functional division Gini coefficient based on the allocation of permanent resident population A study ( 13 , 22 ) conducted in 2021 examined the Gini coefficient of registered nurses in Guangdong province and assessed the fairness of their allocation. However, due to the absence of a division based on prefecture-level cities, there may be some inaccuracies in the calculation using the trapezoid method. The Gini coefficient of registered nurses in the functional division was 0.31, calculated using three different methods: parabolic estimation (0.44), and fitting curve (0.07). The average of three calculation methods was taken, and the Gini coefficient of registered nurses in functional districts was 0.31. The data in Table 5 show the calculated Gini coefficient of functional partition in Guangdong province in 2021, based on the allocation of permanent resident population. ( 1 ) The ladder method ( 12 , 13 , 23 ) $$\text{G}=2{\text{s}}_{\text{A}}=\frac{2}{\text{n}}({\text{y}}_{1}+{2\text{y}}_{2}+\cdots +\text{n}{\text{y}}_{\text{n}})-\frac{\text{n}+1}{\text{n}}$$ $$\text{G}=\frac{2}{4}(0.09+2\times 0.12+3\times 0.13+4\times 0.67)-\frac{4+1}{4}=0.44$$ ( 2 ) The parabolic estimation method By theorem, $$\text{G}=1-\frac{2}{3\text{n}}[1+2({\text{I}}_{2}+{\text{I}}_{4}+\cdots +{\text{I}}_{\text{n}-2})+4({\text{I}}_{1}+{\text{I}}_{3}+\cdots +{\text{I}}_{\text{n}-1}\left)\right]=1-\frac{1}{6}[1+2\times 0.40+4\times (0.09+0.33)=0.42$$ ( 3 ) The curve method, which was fitted by the following formula: $$\left[\begin{array}{ccc}5& \sum {\text{p}}_{\text{i}}& \sum {\text{p}}_{\text{i}}^{2}\\ \sum {\text{p}}_{\text{i}}& \sum {\text{p}}_{\text{i}}^{2}& \sum {\text{p}}_{\text{i}}^{3}\\ \sum {\text{p}}_{\text{i}}^{2}& \sum {\text{p}}_{\text{i}}^{3}& \sum {\text{p}}_{\text{i}}^{4}\end{array}\right]\left[\begin{array}{c}{\text{a}}_{0}\\ {\text{a}}_{1}\\ {\text{a}}_{2}\end{array}\right]=\left[\begin{array}{c}\sum {\text{l}}_{\text{i}}\\ \sum {\text{p}}_{\text{i}}{\text{l}}_{\text{i}}\\ \sum {\text{p}}_{\text{i}}^{2}{\text{l}}_{\text{i}}\end{array}\right]$$ $$\left[\begin{array}{ccc}5& 1.7563& 1.2232\\ 1.7563& 1.2232& 1.0727\\ 1.2232& 1.0727& 1.0251\end{array}\right]\left[\begin{array}{c}{\text{a}}_{0}\\ {\text{a}}_{1}\\ {\text{a}}_{2}\end{array}\right]=\left[\begin{array}{c}1.6192\\ 1.1878\\ 1.0621\end{array}\right]$$ Calculations were performed using the Matlab software. All the fitted curves were as follows: $$\text{G}=1-2{\text{s}}_{\text{B}}=1-2{\int }_{0}^{1}{\phi }\left(\text{p}\right)\text{d}\text{p}=1-2\times ( -0.01+\frac{1}{2}\times 0.79+\frac{1}{3}\times 0.21)=0.07$$ Using the Gini coefficient calculation formula, \(\text{G}\text{i}\text{n}\text{i} \text{c}\text{o}\text{e}\text{f}\text{f}\text{i}\text{c}\text{i}\text{e}\text{n}\text{t}=\frac{\sum \sum \left|{\text{Y}}_{\text{i}}-{\text{Y}}_{\text{j}}\right|}{{\text{n}}^{2}}\) , the MATLAB implementation procedure was as follows: population = [16408;15871.3;15954; 78604]; nurses = [34280; 46368; 53370; 268029]; population_share = population / sum(population); nurses_share = nurses / sum(nurses); population_cumulative = cumsum(population_share); nurses_cumulative = cumsum(nurses_share); gini_nurses = 1 - sum((population_cumulative(1:end-1) + population_cumulative(2:end)) .* diff(nurses_cumulative)); The run result gini_nurses was 0.05. Based on the results of the Gini coefficient calculation, it seems that the fitting curve method was more accurate in calculating the Gini coefficient compared to the other two methods. This suggests that the fitting curve method provides a better estimation of allocation of the registered nurses’ inequality. If the Gini coefficient is close to 0, it indicates that the registered nurse configuration is more equal or balanced. In this case, the configuration can be considered to be in a good equilibrium condition. The fitting curve method can thus be a useful approach for analysing and visualizing the relationship between variables, such as the distribution of registered nurses across different regions based on population. By using a fitting curve, patterns, trends, and imbalances in the configuration of registered nurses can be identified and evaluated. Gini coefficient according to the geographical distribution The results by regional area are shown in Table 6 . According to geographical distribution, the average of three calculation methods was taken, and the Gini coefficient of registered nurses in the functional districts was 0.39. ( 1 ) Ladder method ( 12 , 13 , 23 ) $$\text{G}=2{\text{s}}_{\text{A}}=\frac{2}{\text{n}}({\text{y}}_{1}+{2\text{y}}_{2}+\cdots +\text{n}{\text{y}}_{\text{n}})-\frac{\text{n}+1}{\text{n}}$$ $$\text{G}=\frac{2}{4}(0.09+2\times 0.12+3\times 0.13+4\times 0.67)-\frac{4+1}{4}=0.44$$ ( 2 ) The parabolic estimation method By theorem, $$\text{G}=1-\frac{2}{3\text{n}}[1+2({\text{I}}_{2}+{\text{I}}_{4}+\cdots +{\text{I}}_{\text{n}-2})+4({\text{I}}_{1}+{\text{I}}_{3}+\cdots +{\text{I}}_{\text{n}-1}\left)\right]=1-\frac{1}{6}[1+2\times 0.40+4\times (0.09+0.33)=0.42$$ ( 3 ) The curve method was fitted The idea of calculating the Gini coefficient by fitting the curve method is to use the mathematical method to fit the Lorenz curve, By formula ( 23 ): $$\left[\begin{array}{ccc}5& \sum {\text{p}}_{\text{i}}& \sum {\text{p}}_{\text{i}}^{2}\\ \sum {\text{p}}_{\text{i}}& \sum {\text{p}}_{\text{i}}^{2}& \sum {\text{p}}_{\text{i}}^{3}\\ \sum {\text{p}}_{\text{i}}^{2}& \sum {\text{p}}_{\text{i}}^{3}& \sum {\text{p}}_{\text{i}}^{4}\end{array}\right]\left[\begin{array}{c}{\text{a}}_{0}\\ {\text{a}}_{1}\\ {\text{a}}_{2}\end{array}\right]=\left[\begin{array}{c}\sum {\text{l}}_{\text{i}}\\ \sum {\text{p}}_{\text{i}}{\text{l}}_{\text{i}}\\ \sum {\text{p}}_{\text{i}}^{2}{\text{l}}_{\text{i}}\end{array}\right]$$ $$\left[\begin{array}{ccc}5& 2.28& 1.59\\ 2.28& 1.59& 1.29\\ 1.59& 1.29& 1.15\end{array}\right]\left[\begin{array}{c}{\text{a}}_{0}\\ {\text{a}}_{1}\\ {\text{a}}_{2}\end{array}\right]=\left[\begin{array}{c}1.62\\ 1.30\\ 1.15\end{array}\right]$$ The calculation was made using the Matlab software. The fitted curves were for the following values: Response. $$\text{I}={\phi }\left(\text{p}\right)=0.00+0.09\text{p}+0.91{\text{p}}^{2}$$ $$\text{G}=1-2{\text{s}}_{\text{B}}=1-2{\int }_{0}^{1}{\phi }\left(\text{p}\right)\text{d}\text{p}=1-2\times (0.00-\frac{1}{2}\times 0.09+\frac{1}{3}\times 0.91)=0.30$$ CONCLUSION Without any further information about the methods used in this study, it is challenging to provide a specific evaluation of fairness. Each method may have its own assumptions, limitations, or advantages. It would be helpful to provide more details about the three methods or any additional context to better understand the fairness evaluation of the registered nurse configuration. The results of the analyses conducted in this study showed that there was a regional imbalance in the deployment of registered nurses. The main findings were as follows: ( 1 ) There was an overall increase in the number of registered nurses compared with the number in 2017; however, compared with the targeted number of registered nurses in the 14th Five-Year Plan, there was still a large gap in the number of registered nurses in Guangdong province in 2021; ( 2 ) The quality structure of registered nurses, in terms of titles, showed that there were relatively few nurses with senior titles; ( 3 ) Registered nurses were unevenly distributed among different healthcare organisations and levels; ( 4 ) By index of variation, registered nurses were geographically dispersed; ( 5 ) Registered nurses were relatively unevenly configured geographically by functional division. DISCUSSION The allocation of registered nurses according to the population is relatively fair, but the allocation structure is still not reasonable. The present study showed that there were some differences in the allocation of registered nurses in 21 prefecture-level cities in the Guangdong province. In recent years, with rapid social and economic development, the allocation of medical resources in Guangdong has been continuously strengthened. As the number and quality of nurses have greatly improved, the accessibility and fairness of nursing services have been largely ensured. However, despite this improvement, inadequate configuration and waste exist simultaneously; taking into account different cities’ population size, transportation infrastructure, economic development, and factors such as geographical location, the Pearl River Delta zone in registered nurse resource layout institutions still owe reasonable, affect the fairness and efficiency of medical and health services to provide ( 24 ). Therefore, it is necessary for the government to play a leading role in the allocation of health resources; to strengthen the government’s macro-control ( 25 ); ensure planning, investment, and system construction; focus on the construction of prefecture-level cities that are weak in the allocation of registered nurses; and guide the flow of high-quality nursing resources to areas that are in shortage of the same ( 26 ). Improvement in the optimal allocation of nursing human resources There is still a gap in the functional zoning regarding the equitable configuration of registered nurses. The government and other relevant departments must ensure an optimal allocation of health resources when implementing healthcare plans in the Guangdong province ( 27 – 29 ). First, efforts should be made to improve human resources policies, promote reforms of nursing professionals’ title evaluations, formulate classified and hierarchical evaluation and assessment methods, and improve the evaluation index system. Second, investment regarding the deployment of nursing personnel to the northern ecological zone and coastal economic belt should be increased, the channels of talent flow should be enhanced, nursing teams should be strengthened with an improvement in their performance evaluation and promotion mechanisms, nursing personnel should be treated appropriately, and there should be an improvement in the adjustment mechanism of hidden contradictions, alongside increased professional security for the nursing personnel. Strengthening the ranks of nurses China’s 14th Five-Year Plan for effective health promotion put forward new requirements for nursing development, considering the citizens at the core to provide them with ample health services; address their health needs; build a comprehensive, high-quality, and efficient nursing service system; constantly meet their demands for differentiated nursing services; and ensure an effective nursing career growth. To this end, the number of nurses is expected to increase in the future. Effective measures should thus be taken for the scientific and rational allocation of nurses by medical institutions, according to their functional positioning, service radius, bed size, clinical nursing workload, and technical elements; this would enable organisations to meet the needs of clinical nursing services. A nurse training system should be established after considering the nurses’ demands and competences ( 18 , 30 , 31 ). Moreover, The analysis heavily relies on the availability and accuracy of data related to resource allocation of registered nurses in Guangdong Province. In case of incomplete or unreliable data, the findings of the study may be compromised. And the study may not account for external factors such as government policies, economic conditions, or social factors that could influence resource allocation decisions for registered nurses. These factors could have a significant impact on fairness but may not be fully considered in this analysis. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets and materials analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Fundings Jiangmen Medical and Health Science and Technology Plan Project in 2023[grant numbers 2023YL0401] and Scientific Research Project of Guangdong Provincial Health Economics Association [grant numbers 2022-WJMZ-06, 2022-WJMZ-22, 2022-WJMF-17, 2022-WJMF-26, 2022-WJMF-44]. Authors’ contributions Wei Meng conducted the investigation, designation, methodology, data curation, writing (original draft), writing (review and editing). Xiaomai Wu collected the samples and data and assisted in the investigation. Jingwen He was responsible for methodology and editing. Qinglan kuang 1 collected the samples and data. Fang Li and Xianglan Peng were responsible for project administration, resources, and supervision. All authors reviewed and approved the final manuscript submitted for publication. Acknowledgments The support of colleagues and staff at the Jiangmen Maternity and Child Health Care Hospital are appreciated. References Maier CB. 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Analysis of the fairness of nursing human resource allocation in Jiangsu Province. Chin Nurs Educ. 2017;14(11):868–71. Meng Dijuan XM, Bu Zihan L, Qingyun PX, Zhen X, Guihua. The current quo and fairness of nursing human resources in Jiangsu Province based on centralized index. Chin J Gerontol. 2019;39(17):4345–8. Yearbook ECGH. Guangdong Health and Health Care Yearbook. 2021. Ai F, Wan X. Gini coefficient decomposition-based and mortality-rate-difference-based description of mortality causes in the Chinese population from 1991 to 2019: a retrospective cross-sectional surveillance study. BMJ Open. 2022;12(4):e059395. Pu L. Fairness of the Distribution of Public Medical and Health Resources. Front Public Health. 2021;9:768728. Xinsheng NXY. Two methods for estimating the Gini coefficient. Henan University of Science and Technology; 2010. A S. ON Economic Inequality. Oxford: Oxford University Press; 1997. Li Z, Yang L, Tang S, Bian Y. Equity and Efficiency of Health Resource Allocation of Chinese Medicine in Mainland China: 2013–2017. Front Public Health. 2020;8:579269. Micah AE, Solorio J, Stutzman H, Zhao Y, Tsakalos G, Dieleman JL. Development assistance for human resources for health, 1990–2020. Hum Resour Health. 2022;20(1):51. Ali Q, Yaseen MR, Khan MTI. The impact of temperature, rainfall, and health worker density index on road traffic fatalities in Pakistan. Environ Sci Pollut Res Int. 2020;27(16):19510–29. Wang Yueyue LY, Qin Shang, et al. Research on the fairness of health service resource allocation in China based on agglomeration degree. Health Stat China. 2019;36:874–7. Yu Q, Yin W, Huang D, Sun K, Chen Z, Guo H, et al. Trend and equity of general practitioners' allocation in China based on the data from 2012–2017. Hum Resour Health. 2021;19(1):20. Choi HM, Heo S, Bell ML. The effect modification of greenspace and impervious surface on the heat-mortality association: Differences by the dissimilarity index. Sci Total Environ. 2023;908:168074. Winkelmann J, Muench U, Maier CB. Time trends in the regional distribution of physicians, nurses and midwives in Europe. BMC Health Serv Res. 2020;20(1):937. Noree T, Pagaiya N, Nimnual I. Effect of doctor allocation policies on the equitable distribution of doctors in Thailand. Hum Resour Health. 2023;21(1):1. S S. Measurement of Inequality and Povety. Press OU, editor2001. Xiaoqi N. A proof of equivalence of the three Gini coefficient estimates. J Xinyang Normal Univ. 2009;3:364–. Kim BJ, Choi CJW. Impact of compensation and willingness to keep same career path on burnout among long-term care workers in Japan. Hum Resour Health. 2023;21(1):64. Wakerman J, Humphreys J, Russell D, Guthridge S, Bourke L, Dunbar T, et al. Remote health workforce turnover and retention: what are the policy and practice priorities? 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Tables Table 1 The situation of registered nurses in different regions City Personnel Per Thousand Permanent Residents Doctor-Nurse Ratio Bed Protection Ratio On-The-Job Worker (Person) Health Technicians (Persons) Practicing Assistant/Physician (Person) Registered Nurse (Person) Guangzhou 12.08 9.98 3.52 4.68 1：1.33 1:0.83 Shaoguan 10.3 8.47 2.83 4.14 1：1.46 1:0.56 Shenzhen 7.91 6.41 2.58 2.81 1：1.09 1:0.96 Zhuhai 10.98 9.12 3.51 4.23 1：1.21 1:0.89 Shantou 6.34 5.38 2.15 2.37 1：1.10 1:0.61 Foshan 7.82 6.67 2.39 3.13 1：1.31 1:0.75 Jiangmen 8.4 7.09 2.44 3.35 1：1.37 1:0.63 Zhanjiang 7.9 6.34 2.07 3.02 1：1.46 1:0.49 Maoming 7.13 5.9 2.27 2.75 1：1.21 1:0.44 Zhaoqing 8.72 6.97 2.34 3.13 1：1.34 1:0.64 Huizhou 8.05 6.8 2.62 3.08 1：1.17 1:0.78 Meizhou 8.77 7.26 2.73 3.07 1：1.13 1:0.53 Shanwei 6.35 4.77 1.77 1.87 1：1.06 1:0.41 Heyuan 9.23 7.56 2.51 3.56 1：1.42 1:0.49 Yangjiang 8.51 6.76 2.35 3.06 1：1.30 1:0.49 Qingyuan 7.8 6.51 2.33 3.12 1：1.34 1:0.63 Dongguan 6.95 5.79 2.17 2.77 1：1.27 1:0.85 Zhongshan 7.04 6.08 2.24 2.89 1：1.29 1:0.78 Chaozhou 5.88 4.39 1.85 1.64 1：0.89 1:0.55 Jieyang 5.94 4.84 1.9 2.12 1：1.12 1:0.51 Yunfu 8.12 6.61 2.19 2.95 1：1.35 1:0.60 Table 2 Allocation of Different Categories in Guangdong Province in 2017 and 2021 Item Medical category 2021 year 2017 year Compare to the same period of 2017 year% Average annual growth rate Mechanism type Hospital 264485（71.22%） 211110（68.71%） 25.28% 5.80% Polyclinic 192356（72.73%） 160208（75.89%） 20.07% 4.68% Hospital of traditional chinese medicine 31905（12.06%） 25508（12.98%） 25.08% 5.75% Hospitals of traditional chinese and western medicine 4236（1.60%） 2088（0.99%） 102.87% 19.35% special hospital 35428（13.40%） 23040（10.91%） 53.77% 11.36% nursing home 560（0.21%） 266（0.13%） 110.53% 20.46% Primary medical and health institutions 106900（28.78%） 96132（31.29%） 11.20% 2.69% Hospital level Third-level 164722（62.28%） 113439（53.73%） 45.21% 9.77% Second-level 73113（27.64%） 71186（33.72%） 2.71% 0.67% First-level 13612（5.15%） 15839（7.50%） -14.06% -3.72% other 13038（4.93%） 10646（5.04%） 22.47% 5.2% Table 3 Density of Registered Nurses in Guangdong Province in 2021 City Registered nurse Area (square kilometers) Permanent population (thousands) HRAD(i) PAD(i) HRAD(i)/PAD(i) Guangzhou 88017 7249.27 18810.6 5.43 1.48 3.68 Shaoguan 11844 18412.53 2860.1 0.29 1.31 0.22 Shenzhen 49599 1997.47 17681.6 11.10 0.88 12.54 Zhuhai 10443 1736.46 2464.7 2.69 1.34 2.01 Shantou 13108 2199.15 5530.4 2.66 0.75 3.56 Foshan 30052 3797.72 9612.6 3.54 0.99 3.58 Jiangmen 16220 9506.92 4835.1 0.76 1.06 0.72 Zhanjiang 21258 13262.83 7030.9 0.72 0.95 0.75 Maoming 17084 11427.63 6219.7 0.67 0.87 0.77 Zhaoqing 12912 14891.23 4129.7 0.39 0.99 0.39 Huizhou 18686 11347.39 6066.0 0.74 0.97 0.76 Meizhou 11913 15864.51 3876.9 0.34 0.97 0.35 Shanwei 5030 4865.05 2686.9 0.46 0.59 0.78 Heyuan 10101 15653.63 2840.9 0.29 1.12 0.26 Yangjiang 8026 7955.88 2620.7 0.45 0.97 0.47 Qingyuan 12441 19035.54 3982.8 0.29 0.99 0.30 Dongguan 29174 2460.08 10536.8 5.30 0.87 6.07 Zhongshan 12926 1783.67 4466.9 3.24 0.91 3.55 Chaozhou 4216 3146.11 2574.6 0.60 0.52 1.16 Jieyang 11926 5265.84 5616.8 1.01 0.67 1.51 Yunfu 7071 7785.11 2393.3 0.41 0.93 0.44 Table 4 Density of Functional Zones in Guangdong Province in 2021 Functional division Number of registered nurses Permanent population (thousands) Area (square kilometers) Number of subnation_al units Amplitude of variation (max/min) HRAD(i)/PAD(i) Pearl River Delta Region 268029 78604 54770.21 11 1.69 2.06 East wing coastal economic belt (East wing) 34280 16408.7 18166.19 4 1.45 1.30 West wing of the coastal economic belt (west wing) 46368 15871.3 32646.34 3 1.11 0.70 Northern Ecological Development Zone (Mountain Area) 53370 15954 76751.32 5 1.40 0.30 Table 5 Allocation of registered nurses according to population in functional divisions of Guangdong Province in 2021 Sectorization NRN PRP y i C_NRN C_PRP P_C_PRP P_C_NRN East Wing 34280 16408.7 0.09 34280 16408.7 12.9% 0.09 West Wing 46368 15871.3 0.12 80648 32280 25.5% 0.20 Mountainous Area 53370 15954 0.13 134018 48234 38.0% 0.33 The Pearl River Delta region 268029 78604 0.67 402047 126838 100.0% 1.00 East Wing, East Wing Coastal Economic Belt. West Wing,West Wing Coastal Economic Belt. mountainous area,Northern Ecological Development. NRN,Number of registered nurses.PRP,Permanent resident population (one thousand people). y i, The proportion of registered nurses.C_NRN,Cumulative registered nurse. C_PRP,Cumulative permanent resident population (one thousand people)..P_C_PRP,Cumulative percentage of the permanent resident population(%). P_C_NRN.The cumulative percentage of registered nurses（%） Table 6 Geographic allocation of registered nurses in the functional areas of Guangdong Province in 2021 Sectorization NRN AN y i, C_RN C_AN P_C_AN P_C_RN East Wing 34280 54770.21 0.09 34280 54770.21 30.04% 0.09 West Wing 46368 18166.19 0.12 80648 72936.4 40.00% 0.20 Mountainous Area 53370 32646.34 0.13 134018 105582.74 57.90% 0.33 The Pearl River Delta region 268029 76751.32 0.67 402047 182334.06 100.00% 1.00 East Wing, East Wing Coastal Economic Belt. West Wing,West Wing Coastal Economic Belt. mountainous area. NRN,Number of registered nurses. AN,Area number (km 2 ), y i, The proportion of registered nurses. C_RN,Cumulative registered nurse,C_AN,Cumulative area number (km 2 ). P_C_AN,Percentage of cumulative area area（%）,P_C_RN,The cumulative percentage of registered nurses（%） Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-3795189","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265738278,"identity":"244a2773-87f5-412a-a140-77045ce132b2","order_by":0,"name":"Wei Meng","email":"","orcid":"","institution":"Jiangmen Maternity and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Meng","suffix":""},{"id":265738279,"identity":"9f8246fb-f368-4fb1-b00b-73fed39ec22f","order_by":1,"name":"Xiaomai Wu","email":"","orcid":"","institution":"Jiangmen Maternity and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaomai","middleName":"","lastName":"Wu","suffix":""},{"id":265738280,"identity":"d6b1926c-0020-4a81-acd8-52bec8e2fc42","order_by":2,"name":"Jingwen He","email":"","orcid":"","institution":"Jiangmen Maternity and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"He","suffix":""},{"id":265738281,"identity":"619948ed-84dc-42d2-848e-4bd2f59e9335","order_by":3,"name":"Qinglan Kuang","email":"","orcid":"","institution":"Jiangmen Maternity and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qinglan","middleName":"","lastName":"Kuang","suffix":""},{"id":265738282,"identity":"71326614-eb35-4d70-a082-096962a19ebf","order_by":4,"name":"Xianglan Peng","email":"","orcid":"","institution":"Jiangmen Maternity and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianglan","middleName":"","lastName":"Peng","suffix":""},{"id":265738283,"identity":"920ab431-4cbb-42c4-a289-420fa1e21ee4","order_by":5,"name":"Fang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie3QsUrDQBzH8d/xh0uHC9nkSoY8gXAhEBWLvkokL9BJ106ZrnY1+BJ1cY4EMokv0MFAXyDgErRQ0yvFxaSr4H2Xgz//D9wdYLP9wRSxencSqOCQ3YBpKoqmHSKk9oQnB8JvXnI9QLAnMMQMhIjKER8gDrGP6Sec00zE9UWGiPRrU0Ig8E6KvouRn9+D4kqcqXGGmM3ny3J6jjB/THqIV/iu7si7jmVHJmzhLssHgUSt+gjRlyGVOBBRl4IPEu6L9od0F9M4Si7d2Y7wOyXfEDFdqe6TZe9brhclrcQGaVzR81reInzS6bpp2kng+b8TE8u2KTBSNOZbhDMzk/3rpg2uAKdmzQYIjuzabDbb/+sb9g1VjpZ68kEAAAAASUVORK5CYII=","orcid":"","institution":"Jiangmen Maternity and Child Health Care Hospital","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-12-23 06:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3795189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3795189/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49303605,"identity":"f9280628-f735-4839-935b-b2bc70eb0c03","added_by":"auto","created_at":"2024-01-08 10:39:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eMap of Guangdong province, which illustrates the geographical location and boundaries of Guangdong Province. The map clearly displays the administrative boundaries of Guangdong Province, including the locations of various cities and counties. \u003cstrong\u003eB \u003c/strong\u003eRegion of Guangdong province, which is divided into several parts, providing an overview of the spatial distribution and diversity within the province.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3795189/v1/ba73025a22082ad4d9460cfd.png"},{"id":49303604,"identity":"2abc3964-72a8-4190-9fa0-84f3db3895aa","added_by":"auto","created_at":"2024-01-08 10:39:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eLorenz curve of registered nurse configuration (the population allocation of registered nurses in each city), which depicts the Lorenz curve of the registered nurse configuration in various cities. The x-axis on the graph represents the cumulative percentage of the population, while the y-axis represents the cumulative percentage of registered nurses. The Lorenz curve shows the degree of inequality in the distribution of registered nurses across the cities. By presenting the Lorenz curve, this graph provides insights into the distribution of registered nurses across different cities within the province. \u003cstrong\u003eB \u003c/strong\u003eLorenz curve of registered nurse configuration (registered nurses per square kilometre among cities), which depicts the Lorenz curve of the registered nurse configuration in various cities based on the metric of registered nurses per square kilometer.The x-axis on the graph represents the cumulative percentage of the population, while the y-axis represents the cumulative percentage of registered nurses per square kilometer.The Lorenz curve shows the degree of inequality in the distribution of registered nurses per square kilometer across the cities. The closer the Lorenz curve is to the diagonal line (line of perfect equality), the more evenly distributed the registered nurses per square kilometer are across the cities. If the Lorenz curve deviates significantly from the diagonal line, it indicates a more unequal distribution of registered nurses per square kilometer among the cities.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3795189/v1/4b46172f47c1284a0791d216.png"},{"id":50741593,"identity":"3e677d27-1197-4e65-9efb-dccb23cbaac5","added_by":"auto","created_at":"2024-02-06 16:00:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":778847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3795189/v1/a4e87a09-543a-4175-ab01-ea8be0b5679f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Resource Allocation Fairness of Registered Nurses in the Guangdong Province","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNursing human resources constitute an important part of human resources in the healthcare field (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). They serve as a basis for ensuring the quality of care, human health, and quality of life. They have a direct impact on the development of healthcare, making them an integral part of health and healthcare enterprises. Subsequently, in response to the ageing population in China, there should be a balanced allocation of health resources in the country to ensure that everyone can enjoy high-quality medical resources (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e); this would help in comprehensively promoting the construction of a healthy China. On 14 May 2023, the General Office of the State Council issued Opinions on Promoting the High-Quality Development of Public Hospitals. These opinions clearly state that measures such as reforming the personnel management system, implementing a post-management system, and increasing the number of nurses should be taken to promote the high-quality development of public hospitals. At present, the core problem of nursing human resource allocation lies in ensuring an appropriate number of personnel and structure administration (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo resolve the abovementioned issues in resource allocation, the 14th Five-Year Plan of the People\u0026rsquo;s Republic of China (PRC) has been passed to deepen the country\u0026rsquo;s medical and healthcare reform. Within this outline, the \u0026lsquo;National Economic and Social Development and Long-term Target Outline for 2035\u0026rsquo; aims to increase the number of registered nurses per 1,000 permanent residents in China to 3.8 by 2025. Moreover, the \u0026lsquo;Healthy China 2030\u0026rsquo; planning outline aims to establish a 15-minute basic medical and health service circuit by 2030, striving for 4.7 registered nurses per 1,000 permanent residents. The ultimate goal is to ensure that everyone has equal access to basic medical and health services and that high-quality nursing resources are distributed evenly.\u003c/p\u003e \u003cp\u003eA fair and effective distribution of health resources is essential for the sustainable development of health services (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Nurses play an essential role in medical institutions and their importance cannot be underestimated. China particularly prioritises the development of nurses and strives to achieve a fair distribution of registered nurses. It is ensured that all nurses can provide care based on the same standards and are treated equally under similar conditions. Various strategies have been adopted to improve the professional quality and technical skills of nurses, promote the stability and growth of nursing teams, and ensure the smooth implementation of nursing work. However, there is little research on the regional differences and inefficiency of health resource allocation and health service utilization in China (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNursing has been gradually developed in China, but there are still some issues. First, compared with the demand for medical services, the number of registered nurses in China is insufficient (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and the allocation of nursing human resources is unreasonable. In fact, the number of registered nurses in China accounts for only about 15% of the total number of health professionals, far below the average of 40% in developed countries. Second, the professional quality of registered nurses is not high. With China\u0026rsquo;s economic development and social progress, people\u0026rsquo;s demand for medical services is increasing. However, at present, the quality of registered nurses in China cannot meet people\u0026rsquo;s growing health needs. According to a recent survey(\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), the proportion of nurses with a university degree or higher in China is only about 20%, which lags far behind that in developed countries. Third, the development of nursing dispensation in China is uneven, and nursing products are inconsistent, with significant differences between regions and urban and rural areas.\u003c/p\u003e \u003cp\u003eThis study explores several areas of research to address the problems of inadequate allocation, imbalance, and low quality of nursing staff in the Guangdong province. This research includes assessing the current nursing workforce. A comprehensive assessment of the current nursing workforce in Guangdong province can provide insight into the number of nurses, their qualifications, skills, and areas of dispensation. Various calculation methods have been used to assess the knowledge of the nursing workforce; these methods provide a valuable understanding of the nursing staffing needs and resource allocation in healthcare facilities. By using these methods, healthcare institutions can optimise their staffing levels, improve patient outcomes, and increase the overall efficiency and effectiveness of care delivery. The aim of the analysis was to evaluate the fairness of resource allocation for registered nurses in the Guangdong Province of China. The research question was be: \u0026ldquo;To what extent is resource allocation for registered nurses in the Guangdong Province fair and equitable? Therefore, the findings of this study can help identify gaps in nursing staff allocation and determine the extent of imbalance in different specialties, ultimately improving the quality and safety of nursing care in the region.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe data on health human resources (registered nurses) in medical and healthcare institutions in this study were obtained from the Guangdong Province Health and Family Planning Statistical Yearbook. Regional per capita gross domestic product (GDP) and resident population data were collected from the Guangdong Province Statistical Yearbook(2021). The data on geographic areas were derived from the administrative division information provided by the Ministry of Civil Affairs of Guangdong (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eAccording to the classification provided in the Statistical Yearbook of the Guangdong province (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, map of Guangdong province), Guangdong is divided into four regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003eB): the Pearl River Delta zone, including Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen, and Zhaoqing; east wing of the coastal economic belt (east wing), including Shantou, Shanwei, Jieyang, and Chaozhou; west wing of the coastal economic belt (west wing), including Zhanjiang, Maoming, and Yangjiang; and the northern ecological development zone of Guangdong (mountainous area), including Shaoguan, Heyuan, Meizhou, Qingyuan, and Yunfu (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eGini coefficient\u003c/h2\u003e \u003cp\u003eThe Gini coefficient, G, is a measure of income inequality. According to its criterion, when the Gini coefficient is less than 0.2, the income of residents is considered to be evenly distributed. When it falls between 0.2 and 0.3, the income distribution is considered to be moderately equal. When it ranges from 0.3 to 0.4, the income distribution is considered to be reasonably equal. When it falls between 0.4 and 0.5, the income gap is considered to be large. When it is greater than 0.5, the income gap is considered to be very wide (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this paper, the following formulas were used to calculate the Gini coefficient (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{G}=1-\\frac{2}{3\\text{n}}[1+2({\\text{I}}_{2}+{\\text{I}}_{4}+\\cdots +{\\text{I}}_{\\text{n}-2})+4({\\text{I}}_{1}+{\\text{I}}_{3}+\\cdots +{\\text{I}}_{\\text{n}-1}\\left)\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe fitting curve method was used to solve the problem, while the least square method was used for estimation. The steps used are as follows:\u003c/p\u003e \u003cp\u003eStep 1: Draw points based on the collected data, observe the trend of the discrete points, and determine the type and expression of the fitting function.\u003c/p\u003e \u003cp\u003eStep 2: Calculate the parameters:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{a}}_{0},{\\text{a}}_{1},\\cdots ,{\\text{a}}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eStep 3: Use the formula, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{G}=\\frac{{\\text{S}}_{\\text{A}}}{{\\text{S}}_{\\text{A}}+{\\text{S}}_{\\text{B}}}=1-2{\\text{S}}_{\\text{R}}=1-2{\\int }_{0}^{1}{\\phi }\\left(\\text{p}\\right)\\text{d}\\text{p}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe fitting function selects the algebraic polynomials:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\phi }\\left(\\text{p}\\right)={\\text{a}}_{0}+{\\text{a}}_{1}\\text{p}+{\\text{a}}_{2}{\\text{p}}^{2}+\\cdots +{\\text{a}}_{\\text{m}}{\\text{p}}^{\\text{m}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe equations related to the corresponding fitting function selection of the algebraic polynomial are as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\left[\\begin{array}{cccc}\\text{n}+1\u0026amp; \\sum {\\text{p}}_{\\text{i}}\u0026amp; \\cdots \u0026amp; \\sum {\\text{p}}_{\\text{i}}^{\\text{m}}\\\\ \\sum {\\text{p}}_{\\text{i}}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{2}\u0026amp; \\cdots \u0026amp; \\sum {\\text{p}}_{\\text{i}}^{\\text{m}+1}\\\\ \\cdots \u0026amp; \\cdots \u0026amp; \\cdots \u0026amp; \\cdots \\\\ \\sum {\\text{p}}_{\\text{i}}^{\\text{m}}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{\\text{m}+1}\u0026amp; \\cdots \u0026amp; \\sum {\\text{p}}_{\\text{i}}^{2\\text{m}}\\end{array}\\right]\\left[\\begin{array}{c}{\\text{a}}_{0}\\\\ {\\text{a}}_{1}\\\\ ⋮\\\\ {\\text{a}}_{\\text{m}}\\end{array}\\right]=\\left[\\begin{array}{c}\\sum {\\text{I}}_{\\text{i}}\\\\ \\sum {\\text{p}}_{\\text{i}}{\\text{I}}_{\\text{i}}\\\\ \\sum {\\text{p}}_{\\text{i}}^{2}{\\text{I}}_{\\text{i}}\\\\ ⋮\\\\ \\sum {\\text{p}}_{\\text{i}}^{\\text{m}}{\\text{I}}_{\\text{i}}\\end{array}\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSolve the parameters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{a}}_{0},{\\text{a}}_{1},\\cdots ,{\\text{a}}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e and calculate the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\phi }\\left(\\text{p}\\right)\\)\u003c/span\u003e\u003c/span\u003e (Gini coefficient).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHealth resource density index(HRAD)\u003c/h2\u003e \u003cp\u003eAccording to past literature (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), the health resource aggregation degree (HRAD) can be used to evaluate the fair allocation of medical and health resources. The calculation formula for the concentration degree of health resources, which includes population and geographical area, objectively reflects the balanced degree of registered nurses\u0026rsquo; resource allocation among different groups in the region (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe population agglomeration degree (PAD) refers to the percentage of population concentrated in a specific area, relative to the total land area of that region. It is calculated using the following formula:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\text{H}\\text{R}\\text{A}{\\text{D}}_{\\text{i}}=\\frac{（\\text{H}{\\text{D}}_{\\text{i}}/\\text{H}{\\text{R}}_{\\text{n}}）\\times 100\\text{%}}{（{\\text{A}}_{\\text{i}}/{\\text{A}}_{\\text{n}})\\times 100\\text{%}}=\\frac{\\text{H}{\\text{R}}_{\\text{i}}/{\\text{A}}_{\\text{i}}}{\\text{H}{\\text{R}}_{\\text{n}}/{\\text{A}}_{\\text{n}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{H}\\text{R}\\text{A}{\\text{D}}_{\\text{i}}\\)\u003c/span\u003e \u003c/span\u003e represents the concentration of health resources in a certain area I, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{H}{\\text{R}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e indicates the number of registered nurses, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{A}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e represents the land area, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{A}}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e represents the land area in the upper level area, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{H}{\\text{R}}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e indicates the number of registered nurses in the upper level area.\u003c/p\u003e \u003cp\u003eThe population agglomeration degree (PAD) indicates the proportion of the population gathered in a certain area, which accounts for 1% of the land area of the upper level area. The calculation formula is as follows:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\text{A}{\\text{D}}_{\\text{i}}=\\frac{{(\\text{P}}_{\\text{i}}/{\\text{P}}_{\\text{n}})\\times 100\\text{%}}{({\\text{A}}_{\\text{i}}/{\\text{A}}_{\\text{n}})\\times 100\\text{%}}=\\frac{{\\text{p}}_{\\text{i}}/{\\text{A}}_{\\text{i}}}{{\\text{P}}_{\\text{n}}/{\\text{A}}_{\\text{n}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\text{A}{\\text{D}}_{\\text{i}}\\)\u003c/span\u003e \u003c/span\u003e represents the population density of an area I, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{p}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e represents the population, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{A}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e represents the land area, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{A}}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e represents the land area of the upper level area, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{p}}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e represents the total population of the upper level area.\u003c/p\u003e \u003cp\u003eThe evaluation criteria of the abovementioned calculations are as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{H}\\text{R}\\text{A}{\\text{D}}_{\\text{i}}\u0026gt;1\\)\u003c/span\u003e\u003c/span\u003e: this shows that the distribution of registered nurses by geographical area is fair; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{H}\\text{R}\\text{A}{\\text{D}}_{\\text{i}}}{\\text{P}\\text{A}{\\text{D}}_{\\text{i}}}\\sim1:\\)\u003c/span\u003e\u003c/span\u003e This shows that the allocation of registered nurses is fair, which can basically meet the needs of the local population and the health service is accessible. When \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{H}\\text{R}\\text{A}{\\text{D}}_{\\text{i}}}{\\text{P}\\text{A}{\\text{D}}_{\\text{i}}}\u0026gt;1,\\)\u003c/span\u003e\u003c/span\u003eit shows that registered nurses are in surplus compared to the population size; meanwhile, when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{H}\\text{R}\\text{A}{\\text{D}}_{\\text{i}}}{\\text{P}\\text{A}{\\text{D}}_{\\text{i}}}\u0026lt;1,\\)\u003c/span\u003e\u003c/span\u003ethis shows that the registered nurses are unable to meet the population demand (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLorenz curve\u003c/h2\u003e \u003cp\u003eThe Lorenz curve is used to show the fairness of income distribution of residents in a country or region; it has also been implemented to identify the fairness of health resource allocation (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Drawing on the Lorenz curve, the abscissa refers to the cumulative ratio of permanent population or regional area, and the ordinate represents the cumulative percentage of registered nurses. The fairness of assigned registered nurses is evaluated by the bending degree of the Lorenz curve, and the more the curve bends, the worse it is in terms of fairness (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIndex of dissimilarity\u003c/h2\u003e \u003cp\u003eThe Index of dissimilarity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) (ID) uses the proportion of registered nurses and the corresponding proportion of population or geographical area to evaluate the degree of difference in the allocation of medical and health resources, thus reflecting the fairness of the allocation of medical and health resources in various cities. It is calculated according to the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{I}\\text{D}=\\frac{1}{2}\\sum \\left|{R}_{An}-{R}_{Ap}\\right|,\\)\u003c/span\u003e\u003c/span\u003e in which A is the regional grouping, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{An}\\)\u003c/span\u003e\u003c/span\u003e is the number of registered nurses in Group A that accounts for the proportion; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{Ap}\\)\u003c/span\u003e\u003c/span\u003e is the proportion of the number of permanent residents or geographical area in Group A to the total number of permanent residents or geographical area, respectively. The criterion of difference index is 0\u0026thinsp;~\u0026thinsp;1, and the closer it is to 0, the better the fairness of health resource allocation.\u003c/p\u003e \u003cp\u003eEthical\u003c/p\u003e \u003cp\u003eEquity and Justice. One ethical consideration in resource allocation for registered nurses is ensuring equity and justice. It is important to ensure that resources are distributed fairly among nurses, regardless of factors such as gender, race, or socioeconomic status. This requires considering the needs and capabilities of each nurse and ensuring that they have equal access to necessary resources and opportunities.\u003c/p\u003e \u003cp\u003ePatient Safety and Quality of Care. Another ethical consideration is the impact of resource allocation on patient safety and the quality of care. Nurses play a crucial role in providing safe and effective care, and inadequate allocation of resources can compromise patient safety. Ethical decision-making should prioritize the allocation of resources in a way that ensures optimal patient outcomes and upholds the principles of beneficence and non-maleficence.\u003c/p\u003e \u003cp\u003eContinuous Evaluation and Improvement. Ethical considerations also involve a commitment to continuous evaluation and improvement of resource allocation processes. Regular assessment and feedback from nurses, patients, and other stakeholders can help identify and address any ethical concerns or shortcomings in resource allocation practices, ensuring ongoing fairness and effectiveness.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eRegistered nurses in the region\u003c/h2\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003eDistribution of nurses\u003c/h2\u003e\n \u003cp\u003eThe National Nursing Development Plan (2021\u0026ndash;2025) stated, on 29 April 2022, that the number of registered nurses per thousand population will reach 3.8 in 2025. According to the year-end statistics of 2021, the number of registered nurses per thousand population in Guangdong province was 31,700, and the resident population was about 126.84\u0026nbsp;million. The ratio of doctors to nurses and that of beds to nurses were the main factors reflecting the allocation of registered nurses, which determines their work intensity and nursing quality. The findings showed that at present, there are still some differences between the ratio of doctors to nurses and that of beds to households in medical institutions in Guangdong (Table\u0026nbsp;1).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eRegistered nurse structure\u003c/h2\u003e\n \u003cp\u003eExisting research shows that registered nurses have a rich configuration structure; reasonable professional titles and academic qualifications can reduce nursing costs, improve nursing quality, and reduce the incidence of nosocomial infections. Through data analysis, the configuration structure of registered nurses in Guangdong province was analysed to provide a basis for the rational allocation of nursing human resources. In terms of gender, there were around 96.6% female registered nurses; by age, young people aged 34\u0026ndash;35 years accounted for about 51% of the registered nurses; in terms of academic background, college graduates accounted for about 41.3% of the nurses; and by professional and technical qualifications, the departmental level accounted for 47.2% of the nurses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eConfiguration of different medical institutions\u003c/h2\u003e\n \u003cp\u003eIn 2021, there were more registered nurses in hospitals in the Guangdong province than in primary medical and health institutions, with an average annual growth rate of 2.11% from 2017 to 2021, of which nursing hospitals increased by 111% compared with 2017. Among the types of institutions, the average annual growth rate of registered nurses from 2017 to 2021 was the fastest. According to the hospital level, registered nurses in first-level hospitals showed a downward trend, with a decrease of 14.06% compared with 2017, while the number of registered nurses in third-level hospitals grew the fastest in 2017, with a year-on-year increase of 45.21%. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ch2 align=\"char\" class=\"colspec\"\u003eLorenz curve\u003c/h2\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003cp\u003eIn 2021, Guangdong province had 3.17 registered nurses per 1,000 inhabitants and 2.24 registered nurses per square kilometer (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA Lorenz curve of registered nurse configuration, the population allocation of registered nurses in each city; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB Lorenz curve of registered nurse configuration, registered nurses per square kilometre among cities). Among the cities, Shenzhen, Guangzhou, and Dongguan had a higher number of registered nurses per square kilometre, with 24.38, 12.14 and 11.86 of them, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eMedical resource concentration (HRDI)\u003c/h2\u003e\n \u003cp\u003eAccording to the analysis results of the health resource intensity of registered nurses in various cities of Guangdong province (Table\u0026nbsp;1), it is evident that the density of registered nurses varies greatly according to the geographical allocation level. The density values of Guangzhou, Shenzhen, Zhuhai, Shantou, Foshan, Dongguan, Zhongshan, and Jieyang were all greater than 1, indicating that the registered nurses in these prefecture-level cities were fairly allocated according to geographical area and relatively abundant. Registered nurses in other prefecture-level cities were under-allocated by geographical area and the allocation fairness was poor.\u003c/p\u003e\n \u003cp\u003eBased on past literature (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e), a deep comparison of the concentration of registered nurses in different regions of Guangdong province was conducted based on the population density. The regions were categorized into densely populated areas (PAD\u0026thinsp;\u0026gt;\u0026thinsp;2), areas with moderate population (0.5\u0026thinsp;\u0026lt;\u0026thinsp;PDA\u0026thinsp;\u0026lt;\u0026thinsp;2), and sparsely populated areas (PAD\u0026thinsp;\u0026lt;\u0026thinsp;0.5). The findings showed that the distribution of registered nurses in this region was equitable based on population allocation, and the healthcare resources adequately met the nursing needs of the densely populated areas. When HRAD(i)/PAD(i)\u0026thinsp;\u0026gt;\u0026thinsp;1, this suggests that there is a relative surplus of registered nurses in that particular area compared to the population. When HRAD(i)/PAD(i)\u0026thinsp;\u0026lt;\u0026thinsp;1, it indicates that the registered nurses in this area may lack sufficient resources. Based on the data, the concentration of registered nurses in densely populated areas of Guangzhou and Zhuhai was higher than 1, suggesting that there may be an excess of registered nurses in these areas. (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eDifference index\u003c/h2\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003eConfiguration of registered nurses by functional partition\u003c/h2\u003e\n \u003cp\u003eIn 2021, Guangdong province was divided into four functional divisions based on population and area allocation. The difference index for population allocation was 0.05, while the difference index for area allocation was 0.37. The calculation of the difference index of the four functional divisions showed that the allocation of registered nurses was fair. According to the concentration of registered nurses, the Pearl River Delta region was 2.06, and the east wing coastal economic belt was 1.30, which is relatively dense. (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eGini coefficient of registered nurses by functional division\u003c/h2\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003eGini coefficient based on the allocation of permanent resident population\u003c/h2\u003e\n \u003cp\u003eA study (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e) conducted in 2021 examined the Gini coefficient of registered nurses in Guangdong province and assessed the fairness of their allocation. However, due to the absence of a division based on prefecture-level cities, there may be some inaccuracies in the calculation using the trapezoid method. The Gini coefficient of registered nurses in the functional division was 0.31, calculated using three different methods: parabolic estimation (0.44), and fitting curve (0.07). The average of three calculation methods was taken, and the Gini coefficient of registered nurses in functional districts was 0.31.\u003c/p\u003e\n \u003cp\u003eThe data in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e show the calculated Gini coefficient of functional partition in Guangdong province in 2021, based on the allocation of permanent resident population.\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) The ladder method (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\text{G}=2{\\text{s}}_{\\text{A}}=\\frac{2}{\\text{n}}({\\text{y}}_{1}+{2\\text{y}}_{2}+\\cdots +\\text{n}{\\text{y}}_{\\text{n}})-\\frac{\\text{n}+1}{\\text{n}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$\\text{G}=\\frac{2}{4}(0.09+2\\times 0.12+3\\times 0.13+4\\times 0.67)-\\frac{4+1}{4}=0.44$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) The parabolic estimation method\u003c/p\u003e\n \u003cp\u003eBy theorem,\u003c/p\u003e\n \u003cdiv id=\"Equf\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e$$\\text{G}=1-\\frac{2}{3\\text{n}}[1+2({\\text{I}}_{2}+{\\text{I}}_{4}+\\cdots +{\\text{I}}_{\\text{n}-2})+4({\\text{I}}_{1}+{\\text{I}}_{3}+\\cdots +{\\text{I}}_{\\text{n}-1}\\left)\\right]=1-\\frac{1}{6}[1+2\\times 0.40+4\\times (0.09+0.33)=0.42$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) The curve method, which was fitted by the following formula:\u003c/p\u003e\n \u003cdiv id=\"Equg\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e$$\\left[\\begin{array}{ccc}5\u0026amp; \\sum {\\text{p}}_{\\text{i}}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{2}\\\\ \\sum {\\text{p}}_{\\text{i}}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{2}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{3}\\\\ \\sum {\\text{p}}_{\\text{i}}^{2}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{3}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{4}\\end{array}\\right]\\left[\\begin{array}{c}{\\text{a}}_{0}\\\\ {\\text{a}}_{1}\\\\ {\\text{a}}_{2}\\end{array}\\right]=\\left[\\begin{array}{c}\\sum {\\text{l}}_{\\text{i}}\\\\ \\sum {\\text{p}}_{\\text{i}}{\\text{l}}_{\\text{i}}\\\\ \\sum {\\text{p}}_{\\text{i}}^{2}{\\text{l}}_{\\text{i}}\\end{array}\\right]$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equh\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e$$\\left[\\begin{array}{ccc}5\u0026amp; 1.7563\u0026amp; 1.2232\\\\ 1.7563\u0026amp; 1.2232\u0026amp; 1.0727\\\\ 1.2232\u0026amp; 1.0727\u0026amp; 1.0251\\end{array}\\right]\\left[\\begin{array}{c}{\\text{a}}_{0}\\\\ {\\text{a}}_{1}\\\\ {\\text{a}}_{2}\\end{array}\\right]=\\left[\\begin{array}{c}1.6192\\\\ 1.1878\\\\ 1.0621\\end{array}\\right]$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eCalculations were performed using the Matlab software.\u003c/p\u003e\n \u003cp\u003eAll the fitted curves were as follows:\u003c/p\u003e\n \u003cdiv id=\"Equi\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e$$\\text{G}=1-2{\\text{s}}_{\\text{B}}=1-2{\\int }_{0}^{1}{\\phi }\\left(\\text{p}\\right)\\text{d}\\text{p}=1-2\\times ( -0.01+\\frac{1}{2}\\times 0.79+\\frac{1}{3}\\times 0.21)=0.07$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eUsing the Gini coefficient calculation formula,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{G}\\text{i}\\text{n}\\text{i} \\text{c}\\text{o}\\text{e}\\text{f}\\text{f}\\text{i}\\text{c}\\text{i}\\text{e}\\text{n}\\text{t}=\\frac{\\sum \\sum \\left|{\\text{Y}}_{\\text{i}}-{\\text{Y}}_{\\text{j}}\\right|}{{\\text{n}}^{2}}\\)\u003c/span\u003e\u003c/span\u003e, the MATLAB implementation procedure was as follows:\u003c/p\u003e\n \u003cp\u003epopulation = [16408;15871.3;15954; 78604];\u003c/p\u003e\n \u003cp\u003enurses = [34280; 46368; 53370; 268029];\u003c/p\u003e\n \u003cp\u003epopulation_share\u0026thinsp;=\u0026thinsp;population / sum(population);\u003c/p\u003e\n \u003cp\u003enurses_share\u0026thinsp;=\u0026thinsp;nurses / sum(nurses);\u003c/p\u003e\n \u003cp\u003epopulation_cumulative\u0026thinsp;=\u0026thinsp;cumsum(population_share);\u003c/p\u003e\n \u003cp\u003enurses_cumulative\u0026thinsp;=\u0026thinsp;cumsum(nurses_share);\u003c/p\u003e\n \u003cp\u003egini_nurses\u0026thinsp;=\u0026thinsp;1 - sum((population_cumulative(1:end-1)\u0026thinsp;+\u0026thinsp;population_cumulative(2:end)) .* diff(nurses_cumulative));\u003c/p\u003e\n \u003cp\u003eThe run result gini_nurses was 0.05.\u003c/p\u003e\n \u003cp\u003eBased on the results of the Gini coefficient calculation, it seems that the fitting curve method was more accurate in calculating the Gini coefficient compared to the other two methods. This suggests that the fitting curve method provides a better estimation of allocation of the registered nurses\u0026rsquo; inequality.\u003c/p\u003e\n \u003cp\u003eIf the Gini coefficient is close to 0, it indicates that the registered nurse configuration is more equal or balanced. In this case, the configuration can be considered to be in a good equilibrium condition. The fitting curve method can thus be a useful approach for analysing and visualizing the relationship between variables, such as the distribution of registered nurses across different regions based on population. By using a fitting curve, patterns, trends, and imbalances in the configuration of registered nurses can be identified and evaluated.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eGini coefficient according to the geographical distribution\u003c/h2\u003e\n \u003cp\u003eThe results by regional area are shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. According to geographical distribution, the average of three calculation methods was taken, and the Gini coefficient of registered nurses in the functional districts was 0.39.\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) Ladder method (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv id=\"Equj\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e$$\\text{G}=2{\\text{s}}_{\\text{A}}=\\frac{2}{\\text{n}}({\\text{y}}_{1}+{2\\text{y}}_{2}+\\cdots +\\text{n}{\\text{y}}_{\\text{n}})-\\frac{\\text{n}+1}{\\text{n}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equk\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e$$\\text{G}=\\frac{2}{4}(0.09+2\\times 0.12+3\\times 0.13+4\\times 0.67)-\\frac{4+1}{4}=0.44$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) The parabolic estimation method\u003c/p\u003e\n \u003cp\u003eBy theorem,\u003c/p\u003e\n \u003cdiv id=\"Equl\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equl\" name=\"EquationSource\"\u003e$$\\text{G}=1-\\frac{2}{3\\text{n}}[1+2({\\text{I}}_{2}+{\\text{I}}_{4}+\\cdots +{\\text{I}}_{\\text{n}-2})+4({\\text{I}}_{1}+{\\text{I}}_{3}+\\cdots +{\\text{I}}_{\\text{n}-1}\\left)\\right]=1-\\frac{1}{6}[1+2\\times 0.40+4\\times (0.09+0.33)=0.42$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) The curve method was fitted\u003c/p\u003e\n \u003cp\u003eThe idea of calculating the Gini coefficient by fitting the curve method is to use the mathematical method to fit the Lorenz curve, By formula (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equm\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equm\" name=\"EquationSource\"\u003e$$\\left[\\begin{array}{ccc}5\u0026amp; \\sum {\\text{p}}_{\\text{i}}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{2}\\\\ \\sum {\\text{p}}_{\\text{i}}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{2}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{3}\\\\ \\sum {\\text{p}}_{\\text{i}}^{2}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{3}\u0026amp; \\sum {\\text{p}}_{\\text{i}}^{4}\\end{array}\\right]\\left[\\begin{array}{c}{\\text{a}}_{0}\\\\ {\\text{a}}_{1}\\\\ {\\text{a}}_{2}\\end{array}\\right]=\\left[\\begin{array}{c}\\sum {\\text{l}}_{\\text{i}}\\\\ \\sum {\\text{p}}_{\\text{i}}{\\text{l}}_{\\text{i}}\\\\ \\sum {\\text{p}}_{\\text{i}}^{2}{\\text{l}}_{\\text{i}}\\end{array}\\right]$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equn\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equn\" name=\"EquationSource\"\u003e$$\\left[\\begin{array}{ccc}5\u0026amp; 2.28\u0026amp; 1.59\\\\ 2.28\u0026amp; 1.59\u0026amp; 1.29\\\\ 1.59\u0026amp; 1.29\u0026amp; 1.15\\end{array}\\right]\\left[\\begin{array}{c}{\\text{a}}_{0}\\\\ {\\text{a}}_{1}\\\\ {\\text{a}}_{2}\\end{array}\\right]=\\left[\\begin{array}{c}1.62\\\\ 1.30\\\\ 1.15\\end{array}\\right]$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe calculation was made using the Matlab software.\u003c/p\u003e\n \u003cp\u003eThe fitted curves were for the following values: Response.\u003c/p\u003e\n \u003cdiv id=\"Equo\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equo\" name=\"EquationSource\"\u003e$$\\text{I}={\\phi }\\left(\\text{p}\\right)=0.00+0.09\\text{p}+0.91{\\text{p}}^{2}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equp\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equp\" name=\"EquationSource\"\u003e$$\\text{G}=1-2{\\text{s}}_{\\text{B}}=1-2{\\int }_{0}^{1}{\\phi }\\left(\\text{p}\\right)\\text{d}\\text{p}=1-2\\times (0.00-\\frac{1}{2}\\times 0.09+\\frac{1}{3}\\times 0.91)=0.30$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWithout any further information about the methods used in this study, it is challenging to provide a specific evaluation of fairness. Each method may have its own assumptions, limitations, or advantages. It would be helpful to provide more details about the three methods or any additional context to better understand the fairness evaluation of the registered nurse configuration.\u003c/p\u003e \u003cp\u003eThe results of the analyses conducted in this study showed that there was a regional imbalance in the deployment of registered nurses. The main findings were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) There was an overall increase in the number of registered nurses compared with the number in 2017; however, compared with the targeted number of registered nurses in the 14th Five-Year Plan, there was still a large gap in the number of registered nurses in Guangdong province in 2021; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The quality structure of registered nurses, in terms of titles, showed that there were relatively few nurses with senior titles; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Registered nurses were unevenly distributed among different healthcare organisations and levels; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) By index of variation, registered nurses were geographically dispersed; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Registered nurses were relatively unevenly configured geographically by functional division.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e \u003cb\u003eThe allocation of registered nurses according to the population is relatively fair, but the allocation structure is still not reasonable.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe present study showed that there were some differences in the allocation of registered nurses in 21 prefecture-level cities in the Guangdong province. In recent years, with rapid social and economic development, the allocation of medical resources in Guangdong has been continuously strengthened. As the number and quality of nurses have greatly improved, the accessibility and fairness of nursing services have been largely ensured. However, despite this improvement, inadequate configuration and waste exist simultaneously; taking into account different cities\u0026rsquo; population size, transportation infrastructure, economic development, and factors such as geographical location, the Pearl River Delta zone in registered nurse resource layout institutions still owe reasonable, affect the fairness and efficiency of medical and health services to provide (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Therefore, it is necessary for the government to play a leading role in the allocation of health resources; to strengthen the government\u0026rsquo;s macro-control (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e); ensure planning, investment, and system construction; focus on the construction of prefecture-level cities that are weak in the allocation of registered nurses; and guide the flow of high-quality nursing resources to areas that are in shortage of the same (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eImprovement in the optimal allocation of nursing human resources\u003c/h2\u003e \u003cp\u003eThere is still a gap in the functional zoning regarding the equitable configuration of registered nurses. The government and other relevant departments must ensure an optimal allocation of health resources when implementing healthcare plans in the Guangdong province (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). First, efforts should be made to improve human resources policies, promote reforms of nursing professionals\u0026rsquo; title evaluations, formulate classified and hierarchical evaluation and assessment methods, and improve the evaluation index system. Second, investment regarding the deployment of nursing personnel to the northern ecological zone and coastal economic belt should be increased, the channels of talent flow should be enhanced, nursing teams should be strengthened with an improvement in their performance evaluation and promotion mechanisms, nursing personnel should be treated appropriately, and there should be an improvement in the adjustment mechanism of hidden contradictions, alongside increased professional security for the nursing personnel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eStrengthening the ranks of nurses\u003c/h2\u003e \u003cp\u003eChina\u0026rsquo;s 14th Five-Year Plan for effective health promotion put forward new requirements for nursing development, considering the citizens at the core to provide them with ample health services; address their health needs; build a comprehensive, high-quality, and efficient nursing service system; constantly meet their demands for differentiated nursing services; and ensure an effective nursing career growth. To this end, the number of nurses is expected to increase in the future. Effective measures should thus be taken for the scientific and rational allocation of nurses by medical institutions, according to their functional positioning, service radius, bed size, clinical nursing workload, and technical elements; this would enable organisations to meet the needs of clinical nursing services. A nurse training system should be established after considering the nurses\u0026rsquo; demands and competences (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, The analysis heavily relies on the availability and accuracy of data related to resource allocation of registered nurses in Guangdong Province. In case of incomplete or unreliable data, the findings of the study may be compromised. And the study may not account for external factors such as government policies, economic conditions, or social factors that could influence resource allocation decisions for registered nurses. These factors could have a significant impact on fairness but may not be fully considered in this analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets and materials analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiangmen Medical and Health Science and Technology Plan Project in 2023[grant numbers 2023YL0401] and\u0026nbsp;Scientific Research Project of Guangdong Provincial Health Economics Association [grant numbers\u0026nbsp;2022-WJMZ-06,\u0026nbsp;2022-WJMZ-22, 2022-WJMF-17, 2022-WJMF-26, 2022-WJMF-44].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWei Meng conducted the investigation, designation, methodology, data curation, writing (original draft), writing (review and editing).\u003c/p\u003e\n\u003cp\u003eXiaomai Wu collected the samples and data and assisted in the investigation.\u003c/p\u003e\n\u003cp\u003eJingwen He was\u0026nbsp;responsible for methodology and editing.\u003c/p\u003e\n\u003cp\u003eQinglan kuang\u003csup\u003e1\u003c/sup\u003e collected the samples and data.\u003c/p\u003e\n\u003cp\u003eFang Li and Xianglan Peng were responsible for project administration, resources, and supervision.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript submitted for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe support of colleagues and staff at the Jiangmen Maternity and Child Health Care Hospital are appreciated.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMaier CB. 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Med Soc. 2020;33(02):22\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin Huanhuan QZ. Study on the allocation and fairness of nursing Human Resources in Anhui Province from 2009 to 2017. J Nurs. 2020;35(08):48\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhongliang L. Analysis of the fairness of nursing human resource allocation in Jiangsu Province. Chin Nurs Educ. 2017;14(11):868\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng Dijuan XM, Bu Zihan L, Qingyun PX, Zhen X, Guihua. The current quo and fairness of nursing human resources in Jiangsu Province based on centralized index. Chin J Gerontol. 2019;39(17):4345\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYearbook ECGH. Guangdong Health and Health Care Yearbook. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAi F, Wan X. Gini coefficient decomposition-based and mortality-rate-difference-based description of mortality causes in the Chinese population from 1991 to 2019: a retrospective cross-sectional surveillance study. BMJ Open. 2022;12(4):e059395.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePu L. Fairness of the Distribution of Public Medical and Health Resources. Front Public Health. 2021;9:768728.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXinsheng NXY. Two methods for estimating the Gini coefficient. Henan University of Science and Technology; 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA S. ON Economic Inequality. Oxford: Oxford University Press; 1997.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Yang L, Tang S, Bian Y. Equity and Efficiency of Health Resource Allocation of Chinese Medicine in Mainland China: 2013\u0026ndash;2017. Front Public Health. 2020;8:579269.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMicah AE, Solorio J, Stutzman H, Zhao Y, Tsakalos G, Dieleman JL. Development assistance for human resources for health, 1990\u0026ndash;2020. Hum Resour Health. 2022;20(1):51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli Q, Yaseen MR, Khan MTI. The impact of temperature, rainfall, and health worker density index on road traffic fatalities in Pakistan. Environ Sci Pollut Res Int. 2020;27(16):19510\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Yueyue LY, Qin Shang, et al. Research on the fairness of health service resource allocation in China based on agglomeration degree. Health Stat China. 2019;36:874\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Q, Yin W, Huang D, Sun K, Chen Z, Guo H, et al. Trend and equity of general practitioners' allocation in China based on the data from 2012\u0026ndash;2017. Hum Resour Health. 2021;19(1):20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi HM, Heo S, Bell ML. The effect modification of greenspace and impervious surface on the heat-mortality association: Differences by the dissimilarity index. Sci Total Environ. 2023;908:168074.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinkelmann J, Muench U, Maier CB. Time trends in the regional distribution of physicians, nurses and midwives in Europe. BMC Health Serv Res. 2020;20(1):937.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoree T, Pagaiya N, Nimnual I. Effect of doctor allocation policies on the equitable distribution of doctors in Thailand. Hum Resour Health. 2023;21(1):1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS S. Measurement of Inequality and Povety. Press OU, editor2001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiaoqi N. A proof of equivalence of the three Gini coefficient estimates. J Xinyang Normal Univ. 2009;3:364\u0026ndash;.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim BJ, Choi CJW. Impact of compensation and willingness to keep same career path on burnout among long-term care workers in Japan. Hum Resour Health. 2023;21(1):64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWakerman J, Humphreys J, Russell D, Guthridge S, Bourke L, Dunbar T, et al. Remote health workforce turnover and retention: what are the policy and practice priorities? Hum Resour Health. 2019;17(1):99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitter S, Hamza MM, Alazemi N, Alluhidan M, Alghaith T, Herbst CH. Human resources for health interventions in high- and middle-income countries: findings of an evidence review. Hum Resour Health. 2020;18(1):43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePozo-Martin F, Nove A, Lopes SC, Campbell J, Buchan J, Dussault G, et al. Health workforce metrics pre- and post-2015: a stimulus to public policy and planning. Hum Resour Health. 2017;15(1):14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDlouh\u0026yacute; M. Regional inequalities and substitutability of health resources in the Czech Republic: a five methods of evaluation. Hum Resour Health. 2021;19(1):89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommission NHH. Notice of the National Health Commission on the Issuance of the National Nursing Development Plan (2021\u0026ndash;2025). 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi B, Fu Y, Bai X, Zhang X, Zheng J, Wang Y, et al. Spatial Pattern and Spatial Heterogeneity of Chinese Elite Hospitals: A Country-Level Analysis. Front Public Health. 2021;9:710810.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Songbai LF, Rong Qinglin L, Yiyuan, Liu Shujuan. Research on the construction of the Evaluation Model of Head Nurse's Ability and Quality. Health Inf Manage. 2022;19:195\u0026ndash;204.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e The situation of registered nurses in different regions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.244897959183675%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonnel Per Thousand Permanent Residents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDoctor-Nurse Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBed Protection Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.413793103448278%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOn-The-Job Worker (Person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Technicians (Persons)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.48275862068966%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePracticing Assistant/Physician (Person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegistered Nurse (Person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eGuangzhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e12.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.83\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eShaoguan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eShenzhen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eZhuhai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e9.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eShantou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eFoshan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eJiangmen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eZhanjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eMaoming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eZhaoqing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eHuizhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eMeizhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eShanwei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eHeyuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e9.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eYangjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eQingyuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eDongguan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.85\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eZhongshan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eChaozhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eJieyang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eYunfu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e8.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1：1.35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1:0.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u0026nbsp; Allocation of Different Categories in Guangdong Province in 2017 and 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eMedical category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e2021 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e2017 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eCompare to the same period of 2017 year%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eAverage annual growth rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.204081632653061%\" rowspan=\"7\"\u003e\n \u003cp\u003eMechanism type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e264485（71.22%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e211110（68.71%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e25.28%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e5.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003ePolyclinic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.59090909090909%\"\u003e\n \u003cp\u003e192356（72.73%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e160208（75.89%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e20.07%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e4.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003eHospital of traditional chinese medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.59090909090909%\"\u003e\n \u003cp\u003e31905（12.06%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e25508（12.98%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e25.08%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e5.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003eHospitals of traditional chinese and western medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.59090909090909%\"\u003e\n \u003cp\u003e4236（1.60%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e2088（0.99%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e102.87%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e19.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003especial hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.59090909090909%\"\u003e\n \u003cp\u003e35428（13.40%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e23040（10.91%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e53.77%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e11.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003enursing home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.59090909090909%\"\u003e\n \u003cp\u003e560（0.21%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e266（0.13%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e110.53%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e20.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003ePrimary medical and health institutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.59090909090909%\"\u003e\n \u003cp\u003e106900（28.78%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e96132（31.29%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e11.20%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e2.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.204081632653061%\" rowspan=\"4\"\u003e\n \u003cp\u003eHospital level\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eThird-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e164722（62.28%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e113439（53.73%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003e45.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e9.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003eSecond-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.59090909090909%\"\u003e\n \u003cp\u003e73113（27.64%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e71186（33.72%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e2.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e0.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003eFirst-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.59090909090909%\"\u003e\n \u003cp\u003e13612（5.15%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e15839（7.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e-14.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e-3.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.59090909090909%\"\u003e\n \u003cp\u003e13038（4.93%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e10646（5.04%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\"\u003e\n \u003cp\u003e22.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e5.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u0026nbsp; Density of Registered Nurses in Guangdong Province in 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003eRegistered nurse\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eArea (square kilometers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003ePermanent population (thousands)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003eHRAD(i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003ePAD(i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eHRAD(i)/PAD(i)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eGuangzhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e88017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e7249.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e18810.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e5.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3.68\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eShaoguan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e11844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e18412.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2860.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eShenzhen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e49599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1997.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e17681.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e11.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e12.54\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eZhuhai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e10443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1736.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2464.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e2.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eShantou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e13108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2199.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e5530.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e2.66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eFoshan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e30052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e3797.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e9612.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e3.54\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eJiangmen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e16220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e9506.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e4835.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eZhanjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e21258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e13262.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e7030.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eMaoming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e17084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e11427.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e6219.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eZhaoqing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e12912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e14891.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e4129.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eHuizhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e18686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e11347.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e6066.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.74\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eMeizhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e11913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e15864.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e3876.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eShanwei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e5030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e4865.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2686.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eHeyuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e10101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e15653.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2840.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eYangjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e8026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e7955.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2620.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQingyuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e12441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e19035.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e3982.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eDongguan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e29174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2460.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e10536.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e5.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eZhongshan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e12926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1783.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e4466.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e3.24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eChaozhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e4216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e3146.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2574.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.52\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eJieyang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e11926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e5265.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e5616.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e1.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eYunfu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e7071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e7785.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e2393.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u0026nbsp; Density of Functional Zones in Guangdong Province in 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"113%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eFunctional division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eNumber of registered nurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003ePermanent population (thousands)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003eArea (square kilometers)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eNumber of subnation_al units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003eAmplitude of variation (max/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eHRAD(i)/PAD(i)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003ePearl River Delta Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e268029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e78604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e54770.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eEast wing coastal economic belt (East wing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e34280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e16408.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e18166.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eWest wing of the coastal economic belt (west wing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e46368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e15871.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e32646.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eNorthern Ecological Development Zone (Mountain Area)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e53370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e15954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e76751.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eAllocation of registered nurses according to population in functional divisions of Guangdong Province in 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eSectorization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eNRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003ePRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ey\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003eC_NRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003eC_PRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003eP_C_PRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003eP_C_NRN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eEast Wing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e34280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e16408.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e34280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e16408.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e12.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eWest Wing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e46368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e15871.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e80648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e32280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e25.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;Mountainous Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e53370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e15954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e134018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e48234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e38.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eThe Pearl River Delta region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e268029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e78604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e402047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e126838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003e\u0026nbsp;East Wing, East Wing Coastal Economic Belt. West Wing,West Wing Coastal Economic Belt. mountainous area,Northern Ecological Development. NRN,Number of registered nurses.PRP,Permanent resident population (one thousand people).\u0026nbsp;y\u003csub\u003ei,\u003c/sub\u003eThe proportion of registered nurses.C_NRN,Cumulative registered nurse. C_PRP,Cumulative permanent resident population (one thousand people)..P_C_PRP,Cumulative percentage of \u0026nbsp;the permanent resident population(%). P_C_NRN.The cumulative percentage of registered nurses（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003eGeographic allocation of registered nurses in the functional areas of Guangdong Province in 2021\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"109%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eSectorization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eNRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003eAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ey\u003csub\u003ei,\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003eC_RN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eC_AN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003eP_C_AN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003eP_C_RN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eEast Wing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e34280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e54770.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e34280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e54770.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003e30.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eWest Wing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e46368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e18166.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e80648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e72936.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003e40.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eMountainous Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e53370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e32646.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e134018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e105582.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003e57.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eThe Pearl River Delta region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e268029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e76751.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e402047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e182334.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003eEast Wing, East Wing Coastal Economic Belt. West Wing,West Wing Coastal Economic Belt. mountainous area. NRN,Number of registered nurses. AN,Area number (km\u003csup\u003e\u0026nbsp;2\u003c/sup\u003e),\u0026nbsp;y\u003csub\u003ei,\u003c/sub\u003eThe proportion of registered nurses. C_RN,Cumulative registered nurse,C_AN,Cumulative area\u0026nbsp;number (km\u003csup\u003e\u0026nbsp;2\u003c/sup\u003e). P_C_AN,Percentage of cumulative area area（%）,P_C_RN,The cumulative percentage of registered nurses（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"registered nurse, Guangdong province, medical institution, fair allocation","lastPublishedDoi":"10.21203/rs.3.rs-3795189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3795189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to assess the fairness of resource allocation of registered nurses in the Guangdong Province, based on the Healthy China strategy. It aimed to identify the issues with resource allocation fairness and provide optimisation suggestions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo assess the allocation fairness of registered nurses, the study used the Gini coefficient, health resource density index, Lorenz curve, and index of dissimilarity. Additionally, the study employed three methods to calculate the Gini coefficient to analyse equity among registered nurses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn 2021, the allocation of registered nurses in hospitals accounted for 71.22% of registered nurses in the province, that of registered nurses in general hospitals accounted for 72.73% of the hospitals, and that of registered nurses in tertiary medical institutions accounted for 62.28% of the medical institutions at all levels. In terms of health resource allocation density, the demographic density of registered nurses in Guangzhou and Zhuhai was greater than 1; the difference index was 0.44 and 0.43 by geography and population, respectively. Calculated by population, three methods were used to calculate the Gini coefficient, taking the mean value to be 0.31; according to the geographical distribution, the average of the three calculation methods was taken, and the Gini index of registered nurses in the functional districts was 0.39.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWhen considering the geographical allocation, it is evident that there are disparities in the fair distribution of registered nurses in Guangdong. Specifically, the allocation of registered nurses in the west wing of the coastal economic belt and the ecological development zone (mountain area) of northern Guangdong is insufficient, as indicated by the Gini coefficient of different functional zones. This study recommends improving regional coordinated development to enhance the fairness of registered nurses\u0026rsquo; allocation in the Guangdong province.\u003c/p\u003e","manuscriptTitle":"Analysis of Resource Allocation Fairness of Registered Nurses in the Guangdong Province","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 10:39:20","doi":"10.21203/rs.3.rs-3795189/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff24542e-816c-4bea-a572-67fed83e44aa","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-06T15:52:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 10:39:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3795189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3795189","identity":"rs-3795189","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}