INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
The Impact of Preferential Medical Education System and  
Socioeconomic Status on Substantial GP Development in China  
Dandan Zheng., Norlizah Che Hassan., Norliza Ghazali  
Gui’an New Area University Town, Country Garden Xuefu No.1  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000560  
Received: 28 October 2025; Accepted: 04 November 2025; Published: 18 November 2025  
ABSTRCT  
Objective: This study intends to find out the association between senior high school graduates’ SES and their  
undergraduate students’ program selection and further explore the its impact on medical education system and  
substantial GP development.  
Methods: The study adopted a quantitative method. Specifically, one-way ANOVA was used initially to  
compare the SES among high school graduates from different programs. Then multi-nominal logistic regression  
was used to predict and find out the specific SES changes and accurate odds ratio among different programs.  
Results: The results indicated that there were significant mean differences in SES among the four programs  
(Medical Science; Science & Engineering; Humanities & Arts; Social Science) at P <.05 level [F=5.244, p=.002,  
η2=.045]; The mean score for social science (M = 12.35, SD = 3.36) was significantly different from science &  
engineering (M = 14.06, SD = 4.35). The mean score for social science was significantly different from medical  
science (M = 14.12, SD = 3.14). Moreover, the results of Multinomial Logistic Regression Analysis showed that  
compared with the Social Science program (reference group), higher SES significantly increased the likelihood  
of students entering Humanities & Arts, Science & Engineering, and Medical Science, with the effect being  
strongest for Medical Science.  
Conclusion: This research explored the current situation of medical students’ SES level and illustrated the  
preference of medical education system in China as well as its’ negative impact on GP development and PHC  
(primary healthcare) improvement. It is recommended that comprehensive and thorough reforms are needed  
encompassing both enrollment and education quality aspects.  
Keywords: medical education system; SES; substantial; GP development, PHC  
INTRODUCTION  
According to the Alma-Ata Declaration, the main task of the primary healthcare system (PHC) is to provide  
universally accessible basic healthcare for individuals and families in the community (Hone et al., 2018). As the  
first level in the national health system, it is the key to achieving “Health for all” (Hall & Taylor, 2003). With  
the evolution of the global disease spectrum, mortality spectrum and the trend of population aging, strengthening  
the construction of primary health care system has become the core task of the development of medical and  
health undertakings in various countries (Bitton et al., 2017). Primary health care (PHC) in China is mainly  
provided by primary health care institutions, which provide basic diagnosis and treatment of common and  
frequently-occurring diseases, chronic disease and elderly health management, public health and emergency  
response services (Li et al., 2020).  
General practitioners (GP), as the providers of first contact, continuous, coordinated and comprehensive patient-  
centered health care and treatment services, are the core human resources of the primary health care system, and  
their substantial and sustainable development is the key to realizing the function of hierarchical diagnosis and  
treatment and improving the efficiency of the use of medical and health resources (Kuhlmann et al., 2024). At  
present, the cultivation of general practitioners in China is mainly through continuing education, GP transfer  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
training and “5+3” clinical medicine program, which refers to 5 years’ undergraduate medical education and 3  
years’ standardized residency training (SRT) and in SRT phase, medical graduates completed their initial  
medical career choice, such as GP or other specialists (Su & Zhao, 2023).  
Despite the continuous encouragement of national policies and the vigorous publicity of the society, the  
development of general practitioners in China still faces the serious problems of insufficient quantity and lower  
quality to be improved. First, according to the guidelines issued by the General Office of the State Council, “by  
2030, every 10,000 urban and rural residents will be equipped with five qualified general practitioners.”  
Although the number of general practitioners has been growing in recent years, with 3.28 qualified general  
practitioners per 10,000 urban and rural residents by 2022, a considerable proportion of (assistant) general  
practitioners have only obtained certificates and have not yet registered and obtained practicing qualifications.  
According to regulations, general practitioners who have not obtained practicing qualifications shall not provide  
general practice services.  
Secondly, in the overall general practitioner team, the number of general practitioners who have become general  
practitioners through short-term transfer training accounts for more than half (Yang et al., 2024). The quality of  
these general practitioners is somewhat different from that of general practitioners who have received “5+3”  
undergraduate education and standardized residency training in terms of job competence and professionalism  
(Su & Zhao). The problem of heterogeneity of general practitioners at the grassroots level is more prominent.  
Theoretically, medical education system, as the provider of human resources for the healthcare service market,  
should align its development model and content with market demands (Birch et al., 2009). The different roles of  
general practitioners and specialists in healthcare services necessitate a clear distinction and close cooperation  
between their work domains and service populations to achieve the goals of rational resource utilization and  
effective operation of the healthcare system (Gao et al., 2024). However, the mismatch and misalignment issues  
are prevalent in current healthcare system. Preferential medical education system for specialists and higher SES  
(socioeconomic status) medical graduates hinders the substantial development of general practitioners in China.  
In China, in 2022, average college entrance exam scores of clinical medicine students nationwide is 544.49,  
much higher than national first-tier university college entrance exam average scores line 515. Also in 2022,  
clinical medical students from urban areas accounts for 59.65% and clinical medical students graduating from  
key high schools and model high schools accounts for 50.85% (NCDME, 2022). These data manifest that  
medical students are the elites group compared with students from other disciplines.  
However, among these medical graduates, since GPs are primarily positioned in grassroots healthcare  
institutions, which are generally equipped with poorer working conditions and have limited career prospects,  
numerous studies have stated that GP are usually come from lower socioeconomic status (SES) background.  
Therefore, this study aims to find out the association between senior high school graduates’ SES and their  
undergraduate students’ program selection and further explore the its impact on medical education system and  
substantial GP development. The research questions are proposed as follows:  
RQ1: What are the patterns of university program choices among high school graduates?  
RQ2: How does socioeconomic status (SES) influence high school graduates’ choice of university program?  
RQ3: How does a medical education system shaped by socioeconomic status (SES) influence the development  
and practice of general practitioners (GPs)?  
METHODS  
Methods inquiry should be based on research assumptions (Punch, 2013). This research aims to investigate the  
associations between senior high school students’ SES and their undergraduate program selection, hence,  
quantitative method were employed, as it is quite suitable for ensuring relationships among variables and making  
predictions (Hair et al., 2019a). Additionally, cross-sectional data and correlational design were adopted in this  
research.  
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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
Population and Sampling of the Study  
The target population consists of senior high school graduates in order to illustrate the preference of medical  
education compared with other programs linking with students’ SES. The population consists of 2023 cohort  
senior high school graduates in Guizhou province. According to the statistical data produced by the Ministry of  
Education, the total number of 2023 cohort senior high school graduates in Guizhou province is 318, 658.  
In order to ensure the representativeness of the sample, stratified random sampling was adopted. Firstly, the total  
number of senior high schools in Guizhou Province is 505, which can be divided into 3 types that involve model  
senior high schools, regular senior high schools in urban areas and regular senior high schools in rural areas.  
Then, by applying the ratio and random sampling technique, in total 9 schools were selected from diverse types  
of schools with 4910 students. Next, based on the Research Advisors table and Krejcie and Morgan’s formula  
(Krejcie & Morgan, 1970), 356 graduates was sampled. Finally, according to the ratio, the number of samples  
to be selected from each type of school is determined. The following chart shows the selecting and calculating  
details.  
n = N * Z2 * P * (1 – P) / e2 ÷ [N-1 + Z2 * P * (1 – P) / e2 ]  
where n = the sample to be selected  
N = total population  
Z = critical value (95%) confidence level (1.96)  
P = sample proportion (0.5)  
e = margin error  
n = 4910 * (1.96)2 * 0.5 * (1-0.5) / (0.05)2 ÷ [N-1+ (1.96)2 * 0.5 * (1-0.5) / (0.05)2  
n = 4910* 0.9604/0.0025 ÷ [4910 – 1 + 0.9604/0.0025]  
n = 4910 *384.16 ÷ [4910 – 1 + 384.16]  
n = 4910 *384.16 ÷ 5293.16  
n = 4910 * 0.073  
n = 356  
Thus, 356 students were sampled.  
Table 1 Stratified Random Sampling  
No. of Senior High Schools  
No.  
of Students  
Type of Senior High Schools  
Schools  
Selected  
Students  
Selected  
Model Senior High Schools  
Regular Senior High Schools (urban Areas)  
Regular Senior High Schools (Rural Areas)  
Total  
134  
2
6
1
9
1900  
138  
317  
2760  
200  
54  
250  
18  
505  
4910  
356  
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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
Data collection  
The data of all variables have records in related administrative departments of sampled schools, so this research  
applied secondary data since they are more convincing, objective and benefit the accuracy of the possible results  
better than the survey response.  
At first, a permission and approval letter from a local Institutional Review Board-IRB was obtained before  
conducting this research. Then, for the SES data of 2023 cohort senior high school graduates, the secondary data  
of the sampled graduates were collected based on the archival records from each sample school mainly  
comprising their parents’ education level, parents’ occupation and family’s income. The second variable is  
program selection made by the senior high school graduates, and the data included four dimensions: medical  
science, science and engineering, social science, and humanities and arts.  
Data analysis  
Descriptive analysis serves as the first step for quantitative design. It utilizes the numerical data for describing  
the characteristics of the data set collected from the samples, which provides a clear picture of the data attributes  
and necessary information for the whole research (Villamin et al., 2025). Influential analysis considers the  
association among variables and also can make predictions (Braun & Oswald). Hence, this research adopted  
both descriptive analysis and influential analysis to revolve the proposed research questions.  
Though the sample size is 356, after data collection, and in total 340 samples are obtained, while the missing  
value is 16. Among them, 10 samples lack parental education level, 5 samples have incomplete parental  
occupation information, and 1 sample’s program information is missing. So the data retrieval rate is 95.5%.  
For SES data, the first component is the parental education level, which includes 6 levels (Primary school or  
below; Junior secondary school; Senior secondary school/Vocational secondary school/Technical school; Junior  
college; Bachelor’s degree; Master’s degree or above). Simultaneously, they were sequentially coded from 1 to  
6, ranging from primary school or below (1) to Master’s degree or above (6).  
For the second component, parental occupation, according to Research Report on Contemporary Social  
Stratification in China (Lu, 2002), classified parental occupation into 10 categories: Government officials and  
social administrators; Managers of large and medium-sized enterprises; Owners of small and medium private  
enterprises (small-scale defined as enterprises with 200-300 employees and annual revenue between 3 to 20  
million CNY); Professionals and technical specialists (e.g., engineers, doctors, lawyers, teachers, researchers);  
Lower- and mid-level civil servants, clerks, and general office staff; Self-employed individuals (e.g., small shop  
owners, food service vendors, artisans); Commercial and service workers (e.g., food service staff, salespeople,  
customer service representatives); Industrial workers (e.g., workers in manufacturing and construction sectors);  
Agricultural, forestry, animal husbandry, and fishery workers; Unemployed, underemployed, and jobless  
individuals in urban and rural areas, they were also sequentially coded from 10 to 1, ranging from Government  
officials and social administrators (10) to Unemployed, underemployed, and jobless individuals in urban and  
rural areas (1) (Wang & Wang, 2023).  
For the third component, parental income was divided into five levels, including: Below 5,000 CNY; 5,000-  
8,000 CNY; 8,000-12,000 CNY; 12,000-24,000 CNY; Above 24,000 CNY), and were sequentially coded from  
1 to 5, ranging from Below 5,000 CNY (1) to Above 24,000 CNY) (5). Then according to the SES calculating  
formula from related researchers, SES = (0.82 * parental education level + 0.81 * parental occupation + 0.76 *  
parental income) / 0.63, the score of each sample’s SES is obtained (Sun and Zhou, 2023; Ren, 2010). Next, for  
the data of program selection, all programs be selected by the samples were classified into four categories,  
including social science, coding as 1, humanities and arts, coding as 2, science and engineering, coding as 3 and  
medical science, coding as 4.  
To address the primary research question, SPSS software was employed. Initially, one-way ANOVA was  
conducted to compare the socioeconomic status (SES) of high school graduates from various programs.  
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Subsequently, multinomial logistic regression was utilized to predict and ascertain the specific changes in SES  
and the precise odds ratios across different programs.  
RESULTS  
Demographic information of the respondents  
According to Table 1, the majority of respondents are female, they number 190, and account for 55.88% of the  
total samples, while the males amount to 150, accounting for 44.12%. Among all respondents, 122 are from  
regular high schools in urban areas (35.89%), slightly outnumbering samples from regular schools in rural areas  
(102, 30%). The sizes of samples from national model high schools and provincial model high school are  
relatively low, with a smaller proportion from the national model high school (48, 14.12%).  
Table 2 Demographic Data  
Percentage  
(%)  
Variables  
Category  
Frequency  
Cumulative Percentage (%)  
Gender  
Male  
150  
190  
102  
122  
68  
44.12  
55.88  
30.00  
35.89  
20.00  
14.12  
100  
44.35  
100  
Female  
Type of high  
school  
Regular HS in Rural Areas  
Regular HS in Urban Areas  
Provincial Model HS  
National Model HS  
Total  
30.00  
65.89  
85.89  
100  
48  
340  
100  
(Source. Author’s Creation)  
Patterns of university program choices among high school graduates  
At first, based on the table 2, the mean value of high school graduates’ SES is 13.54, while the standard deviation  
is 3.86 based on the computing value through the formula mentioned in data analysis section. Regarding high  
school graduates’ program selection, we could see that most of them selected science and engineering and social  
science, and the percentages are 32.94% and 32.35%, respectively. This is followed by medical science (91,  
26.76%). while high school graduates selecting humanity and arts program represented the smallest size (27,  
7.94%).  
Table 3 The Results of Descriptive Analysis  
Variables  
SES  
Sample Size (N)  
340  
Minimum  
Maximum  
Mean  
SD  
3.79  
26.70  
13.53  
3.86  
Variables  
Program  
Category  
Frequency Percentage (%) Cumulative Percentage (%)  
Social Science  
Humanities & Arts  
110  
27  
32.35  
7.94  
32.35  
40.29  
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Science and Engineering 112  
32.94  
26.76  
100  
73.23  
100  
Medical Science  
91  
Total  
340  
100  
(Source: Author’s Creation)  
Comparison and Prediction of High School Graduates’ SES and Program Selection  
In response to the second research question, one-way ANOVA was conducted to compare the differences in SES  
by different programs. There were significant mean differences in SES among the four programs (Social Science,  
Humanities & Arts, Science & Engineering, Medical Science) at P<.05 level of significance [F=5.244, p=.002,  
η2=.045]. Post hoc comparison using the Tukey HSD test indicated that the mean score for social science  
(M=12.35, SD=3.36) was significantly different from science & engineering (M=14.06, SD=4.35) at the .05  
level of significance. The mean score for social science (M=12.35, SD=3.36) was significantly different from  
medical science (M=14.12, SD=3.14) at the .05 level of significance. The effect size was interpreted using the  
conventions proposed by Cohen (2013), where partial η2 values of .01, .06, and .14 represent small, medium,  
and large effects, respectively. Hence, the effect size is small (η2=.045), meaning that 4.5% of variance of SES  
is attributed to the program.  
Table 4 The Results of Comparison of SES by Programs  
Programs  
n
Mean  
SD  
F
p
η2  
.002** .045  
5.244  
Social Science  
110  
27  
12.35  
14.11  
14.06  
14.12  
3.36  
4.88  
4.35  
3.14  
Humanities & Arts  
Science & Engineering  
Medical Science  
112  
91  
(Source: Author’s Creation)  
Table 5 Multiple Comparisons  
(I) Programs  
(J) Programs  
Humanities & Arts  
Mean Differences (I-J)  
S.E.  
.81  
.51  
.54  
.81  
.83  
.54  
Sig.  
.14  
Social Science  
-1.77  
-1.71*  
-1.78*  
.06  
Science & Engineering  
Medical Science  
.01**  
.01**  
1.00  
1.00  
.99  
Humanities & Arts  
Medical Science  
Science & Engineering  
Medical Science  
-.01  
Science & Engineering  
-.06  
Note:***P<.001,**P<.01, *P<.05; Dependent variable: SES.  
(Source: Author’s Creation)  
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Furthermore, in order to explore the detailed SES differences of high school graduates in these four programs  
and analyze the relationship between high school graduates’ SES and their program selection as well as  
investigate whether SES can predict high school graduates’ program selection, Multinomial Logistic Regression  
is applied. Based on the results of Table 5, the model’s goodness-of-fit was evaluated using the Likelihood Ratio  
Test (LRT). The test yielded a statistically significant result, this being X²(9) = 77.03, p < .05, indicating that the  
model with predictors provides a significantly better fit to the data than the null model (i.e., a model with only  
the intercept). Therefore the full model is considered as having an acceptable overall fit.  
According to the results of Multinomial Logistic Regression Analysis, relative to social science, under the  
premise of humanity and arts, β= .12, P = .04 (< .05), SES has a significant positive impact on the program. The  
odds ratio (OR) value is 1.13, suggesting that for every one-unit increase in SES, the change in the odds increases  
by 1.13 times (social science---> humanity and arts). Regarding Science and Engineering, β= .09, P = .02 (<  
.05), which means that SES has a significant positive impact on the program. The odds ratio (OR) value is 1.10,  
showing that for every one-unit increase in SES, the change in the odds increases 1.10-fold (social science--->  
Science and Engineering). For Medical Science, β= 0.14, P = .00 (< .05), it means that SES has a significant  
positive impact on the program. The odds ratio (OR) value is 1.15, indicating that for every one-unit increase in  
SES, the change in the odds increases by 1.15 times (Social science---> Medical Science).  
Table 6 Model Fitting Information  
Model  
Intercept  
Final  
Model Fitting Criteria  
811.51  
Chi-Square  
df Sig.  
717.00  
94.51  
15 .00  
(Source: Author’s Creation)  
Table 7 The Results of Multinomial Logistic Regression Analysis  
Humanities &Arts  
SES  
β
S.E.  
.06  
.46  
.
Wald x2  
4.04  
7.68  
.
P
OR  
OR (95% CI)  
1.00~1.28  
1.45~8.64  
.
.04*  
.01  
.
.12  
1.26  
0b  
1.13  
3.54  
.
Male  
Female  
Regular HS in Rural Areas  
Regular HS in Urban Areas  
Provincial Model HS  
National Model HS  
Science & Engineering  
SES  
-.53  
-.32  
-.82  
0b  
.68  
.65  
.93  
.
.68  
.44  
.63  
.38  
.
1.70  
.73  
.44  
.
.44~6.54  
.20~2.62  
.07~2.74  
.
.63  
.77  
.
β
S.E.  
.04  
.31  
.
Wald x2  
5.38  
38.00  
.
P
OR  
1.10  
6.80  
.
OR (95% CI)  
1.02~1.19  
.3.70~12.51  
.
.02*  
.00  
.
.10  
1.92  
0b  
Male  
Female  
Regular HS in Rural Areas  
Regular HS in Urban Areas  
.08  
-.29  
.50  
.46  
.02  
.88  
.53  
1.08  
.75  
.41~2.88  
.31~1.84  
.39  
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Provincial Model HS  
National Model HS  
Medical Science  
SES  
.65  
0b  
.51  
.
1.62  
.20  
.
1.92  
.
.70~5.23  
.
.
β
S.E.  
.04  
.33  
.
Wald x2  
11.64  
.87  
P
OR  
1.15  
1.36  
.
OR (95% CI)  
1.06~1.24  
.71~2.60  
.
.00***  
.35  
.
.14  
.31  
0b  
Male  
Female  
.
Regular HS in Rural Areas  
Regular HS in Urban Areas  
Provincial Model HS  
National Model HS  
1.10  
-.28  
.56  
0b  
.51  
.51  
.56  
.
4.62  
.30  
.03  
.59  
.32  
.
3.01  
.76  
1.74  
.
1.10~8.20  
.28~2.05  
.59~5.16  
.
1.00  
.
Note: ***P<.001,**P<.01, *P<.05; McFadden R² =.11 Cox & Snell R² = .24, Nagelkerke R² =.26;  
a. The reference category is social science; b. This parameter is set to zero because it is referent.  
(Source: Author’s Creation)  
The influence of medical education system shaped by socioeconomic status (SES) on development general  
practitioners (GPs)  
The above research results show that among high school graduates, students who choose the Medical Science  
Program have the highest SES compared with other programs, so it indicates that the medical program has a  
preference compared with other university programs. However, among the high SES group who choose Medical  
Science Program, they generally have higher requirements in terms of career expectations and treatment (Yıldız  
& Khan, 2024; Mitsouras et al., 2019; Torres-Roman et al., 2018), which seriously hinders the possibility of  
choosing GP as a career after graduation. Therefore, the current favored medical education system is not  
conducive to the growth and sustainable development of the general practitioner team.  
DISCUSSION  
The results of SES differences among high school graduates who access various programs demonstrated that the  
mean score of those doing the medicine program is the highest among high school graduates who access other  
programs. It is followed by humanities and arts, science and engineering and social science. It indicates that the  
SES of students who are in the medical program is higher than students from other programs. The results are  
consistent with the previous findings for China and other countries.  
Studies find that there is minimal SES diversity in the whole higher medical education system because students  
from low SES backgrounds confront significant obstacles in their desire to become physicians. AAMC data  
demonstrated a continuously low percentage of medical student matriculants with a parent whose highest level  
of education completed was less than a Bachelor’s degree from 2018 to 2024 (Velasquez et al., 2024). Likewise,  
in China, clinical medical students from urban areas account for 59.65% and clinical medical students graduating  
from key high schools and model high schools account for 50.85% in 2022 (NCDME, 2022). Under this climate,  
higher SES group of medical graduates hardly work as GP at grassroots level.  
Therefore, firstly, widening access to medical education and improving the SES diversity of undergraduates  
majoring in medicine is recommended for both educational equity and healthcare quality and equity. Some  
research also reported that enlarging the “rural pipeline” for students promotes the quantity and quality of GPs,  
and leads to substantial improvement of primary healthcare and contributes to the United Nations SDG goal of  
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“health for all” (Ogden et al., 2020). For example, in China, it should be a policy to expand the enrollment and  
training scale of the rural free-oriented medical student program, and explore the mechanism of aligning graduate  
education in general medicine with standardized residency training in general medicine. This would allow  
students who choose the general practice (including free-oriented medical students) to obtain both a Master’s  
degree certificate and a standardized training certificate after completing three years of standardized residency  
training, thereby increasing the attractiveness of choosing general practice and serving grassroots communities.  
Secondly, the reform or adjustment of medical school education should better adapt to job requirements, with a  
focus on cultivating the competencies needed for general practitioners. This includes emphasizing the increase  
of courses and practical training in general medicine during the education process, covering areas such as chronic  
disease management, health education, emergency public health response, health psychology, and health  
insurance policies. At the same time, it is important to focus on cultivating medical students’ concepts of general  
practice, communication skills, and humanistic qualities in medicine, helping them to develop a generalist  
mindset and habits in general practice.  
Thirdly, the current hardware and software capabilities of general practice education are far behind those of  
specialized medicine. In medical schools, it is necessary to accelerate the establishment and construction of  
dedicated departments of general practice in all medical schools nationwide, while also enhancing the hardware  
facilities of clinical practice training centers and standardized training bases for general practice.  
Fourthly, it is necessary to strengthen the soft capability of general practice faculties, selecting individuals with  
solid teaching abilities and a passion for general practice education as GP instructors. Through centralized and  
unified online and offline training, standardized lesson preparation, and various forms of observation and  
communication, the goal of standardization and across-the-board high quality of general practitioners could be  
achieved.  
CONCLUSIONS  
This research explored the current situation of medical students’ SES level and illustrated the preference of  
medical education system in China as well as its’ negative impact on GP development and PHC improvement.  
It is recommended that comprehensive and thorough reforms are needed encompassing both enrollment and  
education quality aspects. Moreover, all stakeholders including healthcare delivery system, health insurance  
payment system as well as inter-departmental governance are all supposed to be improved to support substantial  
GP development.  
Limotations And Further Research  
Although this study adopted scientific analysis methods, there are still some limitations. First of all, the study  
mainly relies on quantitative analysis, subsequent studies can adopt qualitative analysis methods to deeply  
explore the causes of the current situation. In addition, the sample size of this study is relatively small, and it is  
recommended to expand the scope of the survey. It should be noted that this study only focuses on Guizhou  
Province, China, and its conclusions may have certain limitations in terms of generalization.  
Ethical Approaval  
Ethical approval was granted by the “Research Involving Human Subject Committee” of Guizhou Medical  
University, Guizhou, China.  
Conflict Of Interest  
The authors declare that the research was conducted in the absence of any commercial or financial relationships  
that could be construed as a potential conflict of interest.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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ACKNOWLEDGEMENT  
The authors are thankful to the schools that voluntarily participated in this study.  
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