Mapping Intersectionality in Adolescent Educational Inequality: Gendered and Geographic Disparities in Access to Sex Education in China

Mapping Intersectionality in Adolescent Educational Inequality: Gendered and Geographic Disparities in Access to Sex Education in China | 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 Article Mapping Intersectionality in Adolescent Educational Inequality: Gendered and Geographic Disparities in Access to Sex Education in China MINNE CHEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6154894/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 Educational institutions worldwide face challenges in equitably distributing resources during adolescence, a critical period for identity formation and social development. While centralized educational systems ostensibly promote standardization, less is known about how local implementation may reproduce social inequalities during this crucial stage. Through the lens of intersectionality, this study examines how gender and geographical location interact to shape adolescents' access to health education in centralized systems, using Chinese vocational high schools as an illustrative case. Drawing on data from 3,167 adolescents across regions representing different development levels, we demonstrate how educational institutions may inadvertently perpetuate social stratification through differential access to sex education, even within highly centralized systems. Our findings reveal complex interactions between geographical advantages and gender. Students in more economically developed regions and urban areas show significantly higher odds of receiving comprehensive health education, with particularly pronounced effects for girls in developed regions. Moreover, gender emerges as the primary determinant of exposure to specific developmental content. Girls consistently show higher access across topics, particularly regarding pregnancy and contraception, abortion, and sexual behavior, creating systematic disparities in access to essential knowledge during adolescence. These findings extend theories of educational inequality by revealing how intersecting dimensions of advantage and disadvantage manifest during crucial developmental transitions, even when formal policies mandate equal access, offering insights for policymakers seeking to promote equitable education for adolescents across diverse contexts. Social science/Education Social science/Social policy Social science/Sociology Educational inequality adolescent development social reproduction theory intersectionality gender geographical location Introduction Education has long been viewed as a potential equalizer. Access to quality education, especially during crucial developmental stages from childhood through young adulthood, has long-term implications for reducing inequalities in human development and overall wellbeing globally. Yet, social reproduction theory reveals how educational institutions often perpetuate existing social inequalities, particularly during critical developmental periods like adolescence. Scholars argue that schools systematically reproduce social stratification by distributing educational resources unequally across dimensions such as socioeconomic status, geographical location, and gender (Parsons, 1959 ; Durkheim, 1961; Bourdieu & Passeron, 1977 ). Schools in affluent neighborhoods, for instance, typically offer superior infrastructure, more qualified teachers, and more abundant resources, thereby shaping adolescents' developmental trajectories and social mobility prospects (Domina et al., 2019 ). These inequalities persist even in centralized educational systems, where national policies mandate equal access to educational resources for all students. While centralized policies are designed to ensure standardization, less is known about how local implementation may reproduce social inequalities during crucial developmental stages. Sex education emerges as a critical yet understudied arena for examining these dynamics, with significant implications for adolescent health, identity formation, and social development. Research demonstrates that effective sex education during adolescence promotes sexual and reproductive health while mitigating gender-based violence and fostering inclusivity (Haberland & Rogow, 2015 ; Makleff et al., 2020 ; Bengtsson & Bolander, 2020 ). Yet, scholars have documented that access varies based on institutional policies, resource availability, and ideological influences, with differences emerging across public and private schools, and along dimensions of gender (Fields, 2008 ; Ferguson, 2020 ). Drawing on intersectionality theory (Tefera & Powers, 2018), this study examines how gender and geographical location interact to shape adolescents' exposure to sex education within centralized systems. We use Chinese vocational high schools as an illustrative case, comparing regions representing markedly different socioeconomic contexts (Global Data Lab, 2019; Vogel, 1989 ) to explore how structural inequalities manifest in adolescent health education. Despite China's centralized educational policies and coeducational system that mandates standardized delivery of health education, both gender norms and significant regional variation in economic development shape how these policies are implemented locally. Previous discussions have often portrayed adolescent sex education in such centralized systems as uniformly "insufficient" or "nonexistent" (UNESCO, 2019), implicitly treating its delivery as a homogeneous process. This oversimplified perspective obscures potentially nuanced variations in how gender and geographical location create distinct patterns of educational access and quality during this critical developmental period, making China an ideal setting for examining how local implementation may reproduce social inequalities even within standardized systems. Vocational high schools offer a particularly revealing lens for this investigation. These institutions predominantly serve students from lower socioeconomic backgrounds who are more likely to enter the workforce directly and often engage in sexual relationships earlier than their academically tracked peers (Yu, 2012 ). With more than 16 million adolescents in vocational high schools, comprehensive sex education is not just an academic consideration but a critical developmental intervention for these adolescents' health, social outcomes, and transition to adulthood. Employing first-hand data from the baseline survey of a randomized controlled trial with 3,167 vocational high school students, this study aims to uncover the nuanced ways in which gender and geographical location influence adolescents' access to sex education. By mapping these intersectional inequalities, we contribute to a more sophisticated understanding of how educational disparities emerge and persist during adolescence, even within centralized systems which seemingly promote equity. This study makes three key contributions to the literature on adolescent development and educational inequality. We extend intersectionality theory by demonstrating how gender and geographical location interact to create unexpected patterns of educational access even within centralized systems which seemingly promote equality. This challenges conventional assumptions about how educational policies translate into practice during adolescent development. Empirically, we provide the first systematic evidence of how centralized educational policies create mechanisms that may produce uneven outcomes across different demographic groups during adolescence. Our focus on vocational schools offers unique insights into how educational inequality affects a crucial but understudied population of adolescents who often transition early into the workforce. Our analysis comparing regions at different development levels but under the same policy framework provides a novel approach to understanding how local implementation shapes adolescent educational experiences. This approach reveals patterns of inequality that might be missed in single-region studies. School-based Sex Education in China School-based sex education in China has evolved through various curricular frameworks, from "puberty physiology" and "sexual morality" to broader "health education." The discourse predominantly centers on physiological development and risk prevention, framed through narratives of abstinence, social control, and moral conduct (Liu 1991 ; Liu 2008 ; Pan & Huang 2011). This approach reflects broader societal anxieties about adolescent sexuality, with adults viewing it as a Pandora's box requiring careful management during this developmental period. Within China's education system, sex education remains marginally integrated into the formal curriculum during critical adolescent years. While the Ministry of Education's Guidelines for Health Education encourage incorporating sex education across subjects like Physical Education and Biology (Ministry of Education, 2008 ), implementation faces significant challenges: ambiguous incorporation strategies, minimal allocated hours, and inadequate professional training for educators (Gou et al., 2021 ). Despite China's centralized educational system suggesting uniformity, these implementation gaps create significant variability in how adolescents access sex education, potentially reinforcing existing social inequalities during this formative stage. Geographical Location and Adolescent Educational Inequality In China, adolescents' access to educational resources varies dramatically by geographical location, offering insights into how regional development shapes youth opportunities. Guangdong Province exemplifies how economic development can create distinct educational environments: its history of international trade and economic reforms has established it as a hub of economic and cultural dynamism (Yin, 2003 ; Vogel, 1989 ). In contrast, Yunnan Province presents significant developmental challenges, with limited infrastructure and restricted external influences (Zhang, 2010 ). These structural differences are reflected in development indicators: Yunnan ranks 27th out of 31 mainland provinces in the Human Development Index, while Guangdong stands at 6th, with GDP per capita nearly double that of Yunnan (Global Data Lab, 2019; National Bureau of Statistics, 2022). These provincial disparities shape adolescent sex education delivery. Guangdong has promoted comprehensive sexuality education emphasizing gender equality and adolescent development, particularly in advanced cities like Guangzhou and Shenzhen (Guangdong Education Committee, 2018). Conversely, Yunnan's approach remains conservative, prioritizing abstinence-focused "Life, Survival, Living" education (UNESCO & UNFPA, 2019). The urban-rural divide further compounds these developmental inequities. Urban students consistently receive more comprehensive sex education than their rural counterparts (China Family Planning Association, 2016). Research by Peking University and the Rural Women Development Foundation Guangdong (2018) reveals that rural adolescents face particular challenges: sex education often narrowly focuses on behavior prevention, lacking professional resources and comprehensive guidance. This creates a problematic developmental environment where adolescents encounter misleading health information while lacking structured education and supportive adult guidance. Gender Ideology and Gendered Adolescent Sex Education Gender inequalities persist in Chinese society, with deeply rooted cultural expectations shaping different socialization processes for girls and boys (Attané, 2012 ; Zuo et al., 2018 ). These gender norms profoundly influence adolescent sex education, affecting what adults believe adolescents should learn based on their gender. Research reveals distinct patterns in how sexual information reaches adolescents: girls typically receive more structured education from parents and schools, while boys predominantly learn through mass media (Li et al., 2022 ; Shi et al., 2022 ). The gendered approach manifests in educational practices, with many schools initiating girls' sex education earlier, citing puberty onset (UNESCO & UNFPA, 2019), and implementing gender-segregated instruction. This creates problematic developmental narratives: boys are often framed as potential aggressors, while girls are taught defensive strategies and self-protection (Shi et al., 2022 ; Nack, 2009 ; Boyd, 2010 ). These educational practices reinforce sexual double standards during adolescence. Female virginity receives disproportionate emphasis, reflecting moral judgments that differently evaluate sexual behavior based on gender (Sagebin et al., 2013; Zhang & Rao, 2007 ). Some institutions even implement specialized "girls' education" programs promoting traditional femininity through activities like flower arrangement (Deng, 2012 ), further entrenching gender-based developmental trajectories. Gaps in Past Literature Research on adolescent sex education has focused primarily on measuring outcomes - knowledge, attitudes, and behaviors - across geographical locations and biological sexes (George, 2020; Liang et al., 2019 ; Mbadu Muanda, 2018). This approach leaves critical developmental questions unexplored: Do differences in adolescent outcomes stem from varied educational exposure or other factors? The challenge of distinguishing between school-based education and alternative information sources further obscures how educational institutions may reproduce developmental inequalities during adolescence. Most studies examine sex education within single regions (Chen & Wu, 2013 ; Fonner et al., 2014 ), limiting our understanding of how different contexts shape adolescent experiences. Particularly lacking is research examining the intersectionality between geographical location and gender in adolescent sex education access and delivery. Moreover, vocational high school students remain understudied despite representing a crucial adolescent population. These institutions primarily serve youth from lower socioeconomic backgrounds who may face unique developmental challenges and earlier sexual debut (Yu, 2012 ). This oversight leaves significant gaps in understanding how educational disparities affect adolescent development among more vulnerable populations, particularly in settings where early workforce entry is common. This Study This study examines how gender and geographical location interact to shape adolescents' exposure to sex education within a centralized educational system, focusing on vocational high schools in China's Guangdong and Yunnan provinces. This comparison of regions at different development levels, but operating under the same national policies, offers insights into how local implementation may create educational disparities during adolescence. Drawing on theoretical frameworks of social reproduction and intersectionality, we examine how these factors shape adolescent development opportunities through the following hypotheses: Hypothesis a: Rural adolescents will demonstrate significantly lower levels of school-based sex education exposure compared to their urban counterparts. Hypothesis b: Adolescents in Guangdong province will exhibit higher levels of school-based sex education exposure relative to those in Yunnan province. Hypothesis 2 Male adolescents will report lower levels of school-based sex education exposure compared to female adolescents. Hypothesis 3 The intersectionality of gender and geographical location will reveal a stratified pattern of sex education exposure during adolescence, with girls in Guangdong province experiencing the most comprehensive education, while boys in Yunnan province encounter the most limited interventions. Data and Methods Data The data for this study were obtained from the baseline survey of a randomized controlled trial evaluating the effectiveness of an online comprehensive sexuality education module in enhancing students' knowledge, attitudes, and behaviors related to sexual and reproductive health. A total of 3,358 adolescents from 28 schools participated in the study, completing the survey in April 2019. The sample included 10 schools from Guangdong province and 18 from Yunnan province. The schools varied in size, ranging from approximately 400 to 18,000 students, with most having more than 10 classes. From each school, two to four classes (approximately 100 students) were randomly selected from a list of 10th-grade classes. [Anonymized]. Missing data accounted for 5.69% of the baseline sample and were dropped list-wise. Measurement of Variables Dependent Variables Students' exposure to sex education is measured along two dimensions: (1) the educational stages in which students were exposed to sex education, and (2) the specific topics within sex education that students have received. The stages in which students are exposed to sex education are measured using the question: "At what stage have you received sex education systematically? [Multiple categories can be chosen]." The response categories for this question include "In primary school," "In secondary school," and "In vocational high school." Each response category is considered as one stage. The variable is coded as follows: 1 = had sex education in none of the stages, 2 = had sex education in some of the stages, and 3 = had sex education in all three stages. The topics of sex education that students have received were measured using a variable constructed for students who have received any kind of sex education. Students who have never received any kind of sex education are coded as missing. The variable is measured by the question: "From the first time you started receiving sex education to the present, which of the following sex education have you received? [Multiple categories can be chosen]." The response categories for this question include "Gender," "Reproductive system," "Puberty," "Pregnancy and contraception," "Abortion," "STI and HIV/AIDS," "Sexual behavior," "Sexual violence," and "Love and marriage." Each response category is coded as a binary variable. Independent Variables The independent variables in this study include province, household registration, and students’ biological sex. Biological sex is measured using the question: "What is your biological sex?" The response categories for this question are "Female" and "Male." Girls are coded as 1, and boys are coded as 0. Biological sex was analyzed in the paper as the data do not include information on students' gender identity. Gender identity and gender diversity are not yet prevalent concepts in China, and students' sex education experiences largely align with their biological sex, thus their perceived gender by teachers. Province and household registration are two aspects that likely determine where students have been attending schools. Province is a dummy variable created based on the data collection location. The more economically developed Guangdong province is coded as 1, and the less economically developed Yunnan province is coded as 0. Household registration is an urban-rural dummy variable, and is created based on the question: "Your household registration belongs to..." The response categories for this question include "local city or town," "local rural area," "other city or town," "other rural area," and "I don't know." The dummy variable is created such that "local city or town" and "other city or town" are coded as 1, and "local rural area" and "other rural area" are coded as 0. Household registration serves as a proxy for determining whether the student is from an urban or rural area prior to attending vocational high school. Province and household registration do not overlap with each other. Covariates Individual baseline demographic variables, including ethnicity and average monthly living expenses, were included as covariates in the model. Average monthly living expenses serve as a proxy for the socioeconomic situation of the students' families. Analysis In this study, student observations are nested within classes and schools. Therefore, the three levels will be accounted for in the model. The number of stages at which students are exposed to sex education is analyzed using three-level random-intercept multinomial logistic regression with fixed slopes, using a sample size of 3187. The predicted probabilities of having sex education by stages were then calculated based on the regression output. Next, a subsample of students who have received any form of sex education (N = 1642) is included for the analysis of their exposure to nine sex education topics. The topics that students have received are analyzed using a three-level random-intercept binary logistic regression with fixed slopes. The equations for the nine topic models have the same structure 1 . The predicted probabilities of being exposed to different topics within sex education were then calculated based on the regression output. Results Descriptive Results Table 1 presents the descriptive statistics of the study sample. Among the sample, 45.94% are girls, and slightly over two-thirds are from Yunnan province. Nearly 80% of the sample self-identify as having a birth registration in a rural area, while slightly less than a quarter of the sample belong to ethnic minorities. Table 1 Descriptive Results of the Study Sample For All Students (N = 3187) Biological Sex Frequency % Girl 1464 45.94 Boy 1723 54.06 Province Guangdong 1022 32.07 Yunnan 2165 67.93 Household Registration Urban 643 20.18 Rural 2544 79.82 Ethnicity Minorities 763 23.94 Han 2424 76.06 Living Expense (Mean(SD)) (100 CNY) 6.99 4.82 Stages being exposed to sex education None of the stages 1545 48.48 Some stages 1474 46.25 All stages 168 5.27 For Students who have Received Sex Education (N = 1642) Having received the following content: Puberty 1480 90.13 STI and HIV/AIDS 1335 81.30 Reproductive system 1247 75.94 Gender 1192 72.59 Sexual behavior 992 60.41 Pregnancy and contraception 791 48.17 Sexual violence 494 30.09 Love and marriage 487 29.66 Abortion 387 23.57 Regarding exposure to sex education, almost half of the sample (48.48%) reported that they had never received any form of sex education. Another 46.25% of students indicated that they had received sex education in either primary, secondary, or high school, or in two of these stages. Only 5.27% of students reported receiving sex education in all three stages (primary, secondary, and high school). Among students who had received sex education, the most commonly taught topic was puberty, with 90.13% of students reporting that they had received education on this subject. Additionally, 81.30%, 75.94%, and 72.59% of students indicated that they had received sex education on STI and HIV/AIDS, the reproductive system, and gender, respectively. However, only 60.41% of students had received sex education on sexual behavior. Furthermore, only 48.17%, 30.09%, 29.66%, and 23.57% of students reported receiving sex education on pregnancy and contraception, sexual violence, love and marriage, and abortion, respectively. Bivariate and Multivariate Results Number of Stages Table 2 presents the results of bivariate and multivariate multinomial logistic regression analyses for the stages at which students were exposed to sex education. In the first panel (Some of the Stages vs. None of the Stages), the bivariate analysis shows that living expense has a statistically significant but very small bivariate association with students' likelihood of having sex education in some stages compared to students who have had no exposure to sex education. In the second panel (All of the Stages vs. None of the Stages), when the variables are analyzed individually, girls, students in Guangdong province, and students belonging to the ethnic majority (the Han ethnicity) have higher odds of having sex education in all stages rather than no sex education. Table 2 Bivariate and Multivariate Multinomial Logistic Regression Models for Stages that Students were Exposed to Sex Education (N = 3187) Bivariate Model 1 Model 2 Model3 Some of the Stages vs. None of the Stages Girl 1.05 1.06 1.07 0.93 [0.88;1.26] [0.89;1.26] [0.90;1.27] [0.75;1.15] Guangdong Province 1.38 1.38 1.41 1.18 [0.97;1.97] [0.97;1.96] [0.99;2.00] [0.80;1.74] GirlXGuangdong Province 1.51* [1.04;2.18] Urban 1.04 1.04 1.02 1.03 [0.86;1.26] [0.86;1.26] [0.84;1.23] [0.85;1.25] Minority Ethnicity 0.94 0.91 0.91 [0.77;1.14] [0.75;1.11] [0.74;1.10] Living Expense 1.02* 1.02 1.02* [1.00;1.04] [1.00;1.04] [1.00;1.04] Constant 0.83 0.76* 0.79 [0.66;1.03] [0.58;0.98] [0.61;1.03] All of the Stages vs. None of the Stages Girl 2.02*** 2.03*** 2.03*** 1.21 [1.43;2.85] [1.43;2.87] [1.42;2.89] [0.70;2.09] Guangdong Province 5.09*** 5.09*** 5.65*** 3.37*** [3.21;8.07] [3.21;8.06] [3.32;9.61] [1.72;6.58] GirlXGuangdong Province 2.64** [1.27;5.49] Urban 1.42 1.53* 1.49* 1.53* [0.98;2.07] [1.05;2.25] [1.01;2.20] [1.04;2.26] Minority Ethnicity 0.51** 1.00 0.98 [0.32;0.81] [0.60;1.68] [0.59;1.63] Living Expense 0.98 1.03 1.03 [0.94;1.02] [0.99;1.07] [0.99;1.07] Constant 0.03*** 0.25*** 0.25 [0.02;0.05] [0.01;0.04] [0.02;0.06] Model 1 explores how sex, province, and urban-rural location are related to students' odds of having sex education. Model 2 adds control variables, including ethnicity and living expense. Interactions between significant variables in Model 2 were added to Model 2, and only the interaction between sex and province was found to be significant. Therefore, Model 3 includes the interaction between sex and province. Based on Model 3, the interaction between biological sex and province is significantly related to students' odds of having sex education in some stages (OR = 1.51; CI=[1.04;2.18]) or all stages (OR = 2.64; CI=[1.27;5.49]). This shows that the effect of province on students’ odds of having sex education depends on students’ biological sex. Urban students also have higher odds of being exposed to sex education in all three stages compared to their rural counterparts (OR = 1.53; CI=[1.04;2.26]). Table 3 presents the breakdown of how biological sex, province, and location influence students' predicted probabilities of being exposed to sex education at the three stages. Students in Yunnan province consistently have a higher predicted probability of having no sex education in any of the stages compared to students in Guangdong province. In Guangdong, boys have a higher probability of having no sex education in any of the stages compared to girls. However, there is no substantial difference between boys and girls in Yunnan in terms of having no sex education in any of the stages. There are small differences in the probability of having sex education in some of the stages among different groups. Among students who have had sex education in all stages, the rural-urban difference becomes more pronounced. Students with a household registration in urban areas consistently have a higher probability of having received sex education in all three stages compared to their counterparts in rural areas. Additionally, girls in Guangdong province have significantly higher probabilities than boys from Guangdong province of being exposed to sex education in all three stages. However, the difference between girls and boys in Yunnan province is small. Overall, students in Guangdong province have significantly higher probabilities of having sex education in all stages compared to their counterparts from Yunnan province. Table 3 Predicted Probabilities of Having Sex Education by Stages Guangdong (N = 1022) Yunnan (N = 2165) Girls Boys Girls Boys Stages Urban Rural Urban Rural Urban Rural Urban Rural None 33.18% 35.96% 44.56% 46.47% 52.26% 53.56% 50.89% 52.06% Some 46.33% 49.32% 46.36% 47.28% 43.8% 43.78% 45.94% 45.81% All 20.49% 14.72% 9.09% 6.25% 3.94% 2.65% 3.17% 2.13% Exposure to Sex Education Topics for Students Who have had Sex Education Table 4 presents the bivariate and multivariate binary logistic regression models for the exposure to nine different sex education topics among students who have received any kind of sex education before. Model 1 examines the influence of biological sex, province, and location on the exposure to different sex education topics. Model 2 adds control variables, including ethnicity and living expense. Subsequently, interactions between variables that were significant in Model 2 were tested. However, none of the interactions were found to be significant. Table 4 Bivariate and Multivariate Binary Logistic Regression Models for Exposure to Topics of Sex Education (N = 1642) Puberty STI and HIV/AIDS Reproductive system Bivariate Model 2 Bivariate Model 2 Bivariate Model 2 Girl 2.03*** 2.03*** 1.63*** 1.63*** 1.11 1.10 [1.37;3.01] [1.38;2.98] [1.24;2.15] [1.23;2.15] [0.86;1.44] [0.85;1.43] Guangdong Province 3.61*** 2.95*** 1.11 1.08 1.43 1.36 [2.00;6.52] [1.70;5.12] [0.72;1.71] [0.69;1.67] [0.98;2.09] [0.93;1.99] Urban 1.39 1.39 1.22 1.21 1.84*** 1.82*** [0.87;2.22] [0.87;2.21] [0.87;1.71] [0.86;1.69] [1.33;2.55] [1.31;2.54] Minority Ethnicity 0.50*** 0.60** 0.84 0.87 0.75 0.79 [0.33;0.74] [0.41;0.88] [0.60;1.17] [0.62;1.22] [0.56;1.01] [0.58;1.07] Living Expense 1.00 1.02 1.02 1.02 1.01 1.01 [0.97;1.04] [0.98;1.05] [0.99;1.05] [0.99;1.05] [0.99;1.04] [0.98;1.04] Constant 5.27*** 3.04*** 2.39*** [3.45;8.07] [2.09;4.43] [1.72;3.33] Gender Sexual behavior Pregnancy and contraception Bivariate Model 2 Bivariate Model 2 Bivariate Model 2 Girl 1.40** 1.39** 1.65*** 1.65*** 1.81*** 1.84*** [1.10;1.79] [1.09;1.77] [1.30;2.12] [1.29;2.11] [1.43;2.30] [1.45;2.34] Guangdong Province 1.27 1.11 1.59 1.55 0.96 1.01 [0.90;1.81] [0.78;1.57] [0.97;2.60] [0.94;2.56] [0.57;1.61] [0.60;1.70] Urban 1.27 1.30 1.30 1.30 1.18 1.13 [0.95;1.70] [0.97;1.75] [0.99;1.71] [0.98;1.71] [0.90;1.54] [0.86;1.48] Minority Ethnicity 0.70* 0.74* 0.92 0.98 1.00 1.00 [0.53;0.92] [0.55;0.98] [0.69;1.23] [0.73;1.31] [0.75;1.33] [0.75;1.34] Living Expense 0.99 1.00 1.01 1.01 1.04** 1.04** [0/97;1.02] [0.97;1.02] [0.98;1.03] [0.98;1.03] [1.01;1.06] [1.01;1.06] Constant 2.33*** 0.89 0.47*** [1.71;3.18] [0.61;1.30] [0.32;0.69] Sexual violence Love and marriage Abortion Bivariate Model 2 Bivariate Model 2 Bivariate Model 2 Girl 1.54*** 1.53*** 1.03 1.04 2.21*** 2.21*** [1.21;1,96] [1.20;1.95] [0.82;1.31] [0.82;1.32] [1.66;2.94] [1.66;2.94] Guangdong Province 1.45 1.42 0.97 0.94 1.53 1.43 [0.98;2.14] [0.94;2.14] [0.65;1.43] [0.63;1.41] [0.84;2.84] [0.80;2.54] Urban 1.32* 1.34* 1.39* 1.37* 1.13 1.14 [1.01;1.73] [1.02;1.75] [1.07;1.82] [1.04;1.79] [0.83;1.55] [0.83;1.57] Minority Ethnicity 0.93 1.00 0.83 0.82 0.85 0.90 [0.69;1.24] [0.74;1.36] [0.62;1.11] [0.61;1.10] [0.60;1.20] [0.63;1.28] Living Expense 1.00 1.00 1.01 1.01 1.01 1.01 [0.98;1.03] [0.98;1.03] [0.99;1.04] [0.99;1.04] [0.98;1.04] [0.98;1.04] Constant 0.26*** 0.35*** 0.12*** [0.18;0.37] [0.25;0.49] [0.07;0.19] According to Table 4 , biological sex appears to be a significant factor in predicting the odds of seven out of nine sex education topics. Girls have higher odds of receiving sex education on puberty (OR = 2.03, CI=[1.38;1.98]), STI and HIV/AIDS (OR = 1.63; CI=[1.23;2.15]), gender (OR = 1.39, CI=[1.09;1.77]), sexual behavior (OR = 1.65; CI=[1.29;2.11]), pregnancy and contraception (OR = 1.84, CI=[1.45;2.34]), sexual violence (OR = 1.53; CI=[1.20;2.95]), and abortion (OR = 2.21, CI=[1,66;2.94]) compared to their male counterparts. In one of the nine topics, province is a significant predictor. Students from Guangdong province have higher odds of receiving sex education on puberty (OR = 2.95, CI=[1.70;5.12]) compared to their counterparts in Yunnan province. The urban-rural dummy variable appears to be significant in three of the nine topics. Having a birth registration in urban areas increases the odds of students receiving sex education on reproductive system (OR = 1.82, CI=[1.31;2.54]), sexual violence (OR = 1.34; CI=[1.02;1.75]), and love and marriage (OR = 1.37; CI=[1.04;1.79]). Ethnicity appears to be significant in two of the models. The results show that students of minority ethnicity have lower odds of receiving sex education on puberty (OR = 0.60, CI=[0.41;0.88]) and gender (OR = 0.74, CI=[0.55;0.98]). In Table 5 , we provide an overview of how sex, province, and urban-rural location influence students' likelihood of being exposed to different topics in sex education. Only factors that were found to be significant in the multivariate analyses are included in the predictions. Table 5 Predicted Probabilities of Having Sex Education on Different Topics (N = 1642) Topic Significant Predictor Probability Puberty Sex Girl 93.13% Boy 87.31% Province Guangdong 96.20% Yunnan 87.39% STI and HIV/AIDS Sex Girl 84.31% Boy 77.03% Reproductive System Location Urban 82.96% Rural 73.19% Gender Sex Girl 75.38% Boy 69.06% Sexual Behavior Sex Girl 64.28% Boy 53.39% Pregnancy and Contraception Sex Girl 53.22% Boy 39.73% Sexual Violence Sex Girl 33.83% Boy 25.44% Location Urban 34.12% Rural 28.29% Love and Marriage Location Urban 33.59% Rural 27.27% Abortion Sex Girl 27.34% Boy 15.59% The findings suggest that students who have received any form of sex education generally have a lower chance of being taught topics related to pregnancy and contraception, abortion, sexual violence, and love and marriage. However, they have a higher chance of being taught topics on gender, reproductive system, puberty, and STI and HIV/AIDS. Students have a moderate chance of being taught about sexual behavior. According to Table 5 , girls consistently have a significantly higher probability of being introduced to various topics in sex education compared to boys. Specifically, girls have a higher probability of being taught about puberty (93.13% vs. 87.31%), STI and HIV/AIDS (84.31% vs. 77.03%), gender (75.38% vs. 69.06%), sexual behavior (64.28% vs. 53.39%), pregnancy and contraception (53.22% vs. 39.73%), sexual violence (33.83% vs. 25.44%), and abortion (27.34% vs. 15.59%). The difference in the probabilities for girls and boys in terms of receiving education on pregnancy and contraception, abortion, and sexual behavior exceeds 10%, which is larger compared to other topics. Additionally, students with a household registration in urban areas also have a higher chance of receiving sex education on reproductive system (82.96% vs. 73.19%), sexual violence (33.83% vs. 25.44%), and love and marriage (33.59% vs. 27.27%) compared to their rural counterparts. Moreover, students in Guangdong province have a higher chance of receiving sex education on puberty (96.20% vs. 87.39%) compared to students in Yunnan province. However, urban-rural location and province are not as significant as gender in influencing the types of topics received. Discussion This study extends our understanding of how educational institutions reproduce social inequalities during adolescence, even within centralized systems which seemingly promote equity. By examining the intersection of gender and geographical location in Chinese vocational schools, we reveal systematic patterns of educational inequality that may significantly impact adolescent development trajectories. Our findings demonstrate that despite standardized national guidelines, access to crucial health education during adolescence remains stratified along multiple dimensions, with particularly pronounced effects for students in less developed regions and significant gender-based disparities in exposure to essential knowledge. Our results extend social reproduction theory by demonstrating how geographical advantages compound with gender to create distinct educational trajectories during adolescence. Adolescents in the economically developed Guangdong province and those with urban registrations show higher odds of receiving sex education across multiple school stages. This pattern reveals how regional economic disparities translate into educational inequalities even within a highly unified policy framework. The intersectionality of these disparities is particularly evident among girls in Guangdong province, who show significantly higher odds of receiving sex education compared to all other groups. Research demonstrates that effective sex education during adolescence enhances sexual knowledge, attitudes, and risk-reducing behaviors (Kedzior et al., 2020 ; Wadham et al., 2019 ; Fonner et al., 2014 ; Kirby et al., 2007 ), while promoting healthy relationship development (Goldfarb et al., 2021). Given the importance of early and sequential sex education during adolescent development (Goldfarb et al., 2021), uneven access may amplify existing inequalities between developed and less developed regions (Yang et al., 2014 ), affecting both health knowledge and overall well-being (Harris, 2010 ; Domina et al., 2019 ). These disparities create a cycle of developmental disadvantage that extends beyond education. Limited sexual health knowledge during adolescence increases risks of unintended pregnancies and early school departure, restricting future opportunities. In underdeveloped areas, the absence of comprehensive sex education perpetuates harmful gender norms and gender-based violence, particularly affecting adolescent girls' developmental trajectories. While geographical location influences adolescents' access to sex education across developmental stages, students' sex, thus their perceived gender by teachers, emerges as the primary determinant of exposure to specific topics. Despite China's predominantly co-educational system, girls consistently show higher exposure across almost all topics. The disparity is particularly pronounced in topics such as pregnancy and contraception, abortion, and sexual behavior, where the difference in predicted probabilities between boys and girls exceeds 10%. This pattern suggests that sex education content during adolescence is primarily directed toward girls, with boys often excluded from crucial developmental discussions. These findings reflect broader societal gender expectations, where girls are positioned as responsible for "refusing boys' sexual advances" and "protecting themselves" (Shi et al., 2022 ; Nack, 2009 ; Boyd, 2010 ). However, this gendered approach carries significant developmental consequences. It places an unfair burden on girls regarding sexual responsibility, subjects them to harsher moral judgments when these expectations aren't met (Zhang & Rao, 2007 ), and reinforces a protective framework that ultimately limits women's ability to compete with men (Rury, 1987 ). The emphasis on traditional gender roles and the construction of femininity and masculinity during this formative period may also contribute to increased rates of family violence later in life (Mshweshwe, 2020 ). The relative exclusion of boys from comprehensive sex education during adolescence has troubling developmental implications. Given that boys are more likely to engage in early sexual debut and risky behaviors (Li et al., 2022 ; Shi et al., 2022 ), limited access to sex education may compromise their healthy development. This concern extends to vulnerable populations - since 2010, men who have sex with men have shown the highest HIV prevalence (Wu et al., 2019 ), with significant rates of intimate partner violence (Wei et al., 2019). Research indicates that mixed-gender approaches to sex education, particularly during adolescence, yield better developmental outcomes by facilitating direct communication between boys and girls (Pacifici et al., 2001 ; Clinton-Sherrod et al., 2009 ; Felty et al., 1991). Our research also reveals concerning gaps in adolescent health education that have significant implications for development theory and practice. The finding that only 5% of students receive continuous sex education across all developmental stages challenges assumptions about the standardizing effects of centralized education systems. This discontinuity in health education during critical developmental periods may have lasting implications for adolescent health outcomes and social development, particularly affecting students from less advantaged backgrounds who may lack alternative sources of health information (UNFPA, 2018). This study's focus on vocational high schools illuminates how educational inequalities affect a vulnerable adolescent population during their transition to adulthood. Our findings demonstrate how intersectionality theory can reveal unexpected patterns of advantage and disadvantage within uniform educational systems, while suggesting practical implications for policy and practice. The significant regional variations and gender-based patterns in educational access indicate a need for targeted interventions and additional resources in less developed areas, alongside reconsidering how health education is delivered to ensure comprehensive coverage for both boys and girls. This study has two main limitations. First, as the data come from the baseline survey of a randomized controlled trial, representativeness may be limited (Matthews, 2006 ). While we included adolescents from both urban and rural settings across provinces of varying development levels, the schools were primarily from highly developed cities in each province. Future research should seek more representative samples and explore comparisons with academic high schools, where students' privileged backgrounds may not necessarily translate to better sex education access due to exam-focused curricula. Second, although 72.95% of students receiving sex education reported learning about gender, whether the content reinforces traditional roles or promotes equality and diversity remains unclear (UNFPA, 2008). Future studies should examine these nuances within gender education and their impacts on adolescent development. Conclusion Moving beyond simplistic narratives about educational access, this study is the first to demonstrate how existing inequalities along gender and geographical lines are reproduced through educational institutions during adolescence, even within a centralized system which seemingly promotes equity. By examining the intersection of geographical location and gender in Chinese vocational schools, we reveal systematic patterns of educational inequality that may significantly impact adolescent development trajectories. These findings suggest how local implementation of centralized policies can reproduce and potentially amplify existing social disparities, with implications for understanding educational inequality more broadly. The study reveals that despite standardized guidelines, access to crucial health education during adolescence remains stratified, particularly affecting students in less developed regions and creating gendered patterns of exposure to essential knowledge. These patterns suggest the need for targeted interventions that consider both geographical and gender-based barriers to educational access. The findings have transnational relevance for other centralized educational systems, whether at the national or local level, seeking to address similar inequalities in adolescent education. From a policy perspective, our research suggests two key interventions. First, additional resources should be allocated to vocational schools in less developed regions to ensure standardized implementation of health education curricula. Second, teacher training programs should address implicit gender biases that may influence content delivery during adolescence. Future research should examine how these educational inequalities influence long-term developmental outcomes across different geographical and gender groups, with particular attention to vulnerable adolescent populations who may face unique challenges in their transition to adulthood. Understanding these intersecting dimensions of inequality is crucial for developing effective policies that can better support adolescent development across diverse contexts. Declarations Ethics approval statement The original trial, which data of the current study comes from, was approved by the [Anonymized] in accordance with the Declaration of Helsinki. Informed consent statement During the data collection process of the original trial, written informed consent was obtained at both the institutional level from participating schools and the individual level from students' guardians before data collection began in March 2019. The consent covered participation, data usage, and permission to publish. All participants were fully informed about the purpose of the research, how their data would be used, any potential risks of participation, and that their anonymity would be strictly protected. Author Contribution MC participated in the original randomized controlled trial as the key research assistant, responsible for leading, designing and implementing the study, collecting and analyzing data, and writing reports. MC also contributed to the funding acquisition, conceptualization, analysis, and interpretation of the data for the current study within the scope of this manuscript, which utilized the baseline survey data from the randomized controlled trial. Additionally, MC authored the first draft of the manuscript and managed subsequent revisions. All listed author(s) have made substantial contributions to the manuscript and have agreed to the final submitted version. Acknowledgement The author would like to thank Nanyang Technological University (Grant No. 024273-00001) for supporting the analysis and submission of the current manuscript; Xi’an Guangyuan Sex Education Support Charity Centre (Grant No. MSICKCF20172020001), which funded the original randomized controlled trial; Professor Kun Tang, the Principal Investigator of the cluster randomized controlled trial, from Tsinghua University Vanke School of Public Health, for support during the design and execution of the original randomized controlled trial, in which the author participated as the key research assistant responsible for the project; Marie Stopes International China and Professor Kun Tang for sharing the data; and all volunteers and team members, especially Tong Xin and Xueli Qiu, who have altruistically contributed to the study. The author would like to thank the schools, teachers and students enrolled in the study for their support and trust. The author would like to thank Dr. Catherine Zimmer for her help and guidance on the statistical analyses conducted in the study and Dr Yong Cai and Dr. Kathleen Mullan Harris for their valuable feedback on the manuscript.The author acknowledges the use of Claude 3.5 Sonnet for language improvement purposes. Data Availability The author of the current paper does not own the data, but data may be available from the Principal Investigator of the original randomized controlled trial, Dr. Kun Tang ( [email protected] ), or Marie Stopes International China Office, another owner of the data on reasonable request. References Attané I (2012) Being a woman in China today: A demography of gender. China Perspect 2012(4):5–15 Bengtsson J, Bolander E (2020) Strategies for inclusion and equality–‘norm-critical’ sex education in Sweden. Sex Educ 20(2):154–169 Bourdieu P, Passeron J-C (1977) Reproduction: In education, society and culture. 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Reproductive Health 15(1):1–10 Footnotes The equation for the three-level random-intercept binary logistic regression model is: \:\text{l}\text{o}\text{g}\text{i}\text{t}\left\{\text{Pr}\left(\text{y}\text{ijk}=1|\mathbf{x}\text{ijk},\:{{\upzeta\:}\text{jk}}^{\left(2\right)},{{\upzeta\:}\text{k}}^{\left(3\right)}\right)\right\}=\left({\beta\:}_{1}+\:{{\upzeta\:}\text{jk}}^{\left(2\right)}+\:{{\upzeta\:}\text{k}}^{\left(3\right)}\right)+\:{\beta\:}_{2}{x}_{2ijk}+\dots\:+\:{\beta\:}_{6}{x}_{6ijk}\: 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. 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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-6154894","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471475854,"identity":"2436f62a-1a1b-449f-acfb-143bd15167ec","order_by":0,"name":"MINNE CHEN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACZgiVAGQdgDAPEK+FLQGimqAWBrgWHgPitOi28x578IHBLo9fuufb449tDHJ8NxKYP37Bo8XsMF+64QyG5GLJOWe3GxxsYzCWvJHAJi2DVwuPmTQPw4HEDTdyt0kAtQAZCWzMEoS0/AFq2X8j5xlISz1QC/NngloYQLZI5LCBtCQY3EhgkPxAQItkj0Fy4owbaWYSZ85JGM4887BNGo8OBrPzZ8wkflTYJfbPSH4mUVFmI893PPnwxx/49ICBAZwF8gRjAzMPQS3ogJGwLaNgFIyCUTCCAAAVlU2dQyUK5QAAAABJRU5ErkJggg==","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":true,"prefix":"","firstName":"MINNE","middleName":"","lastName":"CHEN","suffix":""}],"badges":[],"createdAt":"2025-03-04 13:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6154894/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6154894/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85379638,"identity":"a5e861ae-2bb5-43b1-a6c2-294c0875a538","added_by":"auto","created_at":"2025-06-25 09:02:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1098981,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6154894/v1/c8b68dfc-9c4e-497d-877a-8e1e9e72c95b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping Intersectionality in Adolescent Educational Inequality: Gendered and Geographic Disparities in Access to Sex Education in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEducation has long been viewed as a potential equalizer. Access to quality education, especially during crucial developmental stages from childhood through young adulthood, has long-term implications for reducing inequalities in human development and overall wellbeing globally. Yet, social reproduction theory reveals how educational institutions often perpetuate existing social inequalities, particularly during critical developmental periods like adolescence. Scholars argue that schools systematically reproduce social stratification by distributing educational resources unequally across dimensions such as socioeconomic status, geographical location, and gender (Parsons, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1959\u003c/span\u003e; Durkheim, 1961; Bourdieu \u0026amp; Passeron, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Schools in affluent neighborhoods, for instance, typically offer superior infrastructure, more qualified teachers, and more abundant resources, thereby shaping adolescents' developmental trajectories and social mobility prospects (Domina et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese inequalities persist even in centralized educational systems, where national policies mandate equal access to educational resources for all students. While centralized policies are designed to ensure standardization, less is known about how local implementation may reproduce social inequalities during crucial developmental stages. Sex education emerges as a critical yet understudied arena for examining these dynamics, with significant implications for adolescent health, identity formation, and social development. Research demonstrates that effective sex education during adolescence promotes sexual and reproductive health while mitigating gender-based violence and fostering inclusivity (Haberland \u0026amp; Rogow, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Makleff et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bengtsson \u0026amp; Bolander, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Yet, scholars have documented that access varies based on institutional policies, resource availability, and ideological influences, with differences emerging across public and private schools, and along dimensions of gender (Fields, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ferguson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDrawing on intersectionality theory (Tefera \u0026amp; Powers, 2018), this study examines how gender and geographical location interact to shape adolescents' exposure to sex education within centralized systems. We use Chinese vocational high schools as an illustrative case, comparing regions representing markedly different socioeconomic contexts (Global Data Lab, 2019; Vogel, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) to explore how structural inequalities manifest in adolescent health education. Despite China's centralized educational policies and coeducational system that mandates standardized delivery of health education, both gender norms and significant regional variation in economic development shape how these policies are implemented locally. Previous discussions have often portrayed adolescent sex education in such centralized systems as uniformly \"insufficient\" or \"nonexistent\" (UNESCO, 2019), implicitly treating its delivery as a homogeneous process. This oversimplified perspective obscures potentially nuanced variations in how gender and geographical location create distinct patterns of educational access and quality during this critical developmental period, making China an ideal setting for examining how local implementation may reproduce social inequalities even within standardized systems.\u003c/p\u003e \u003cp\u003eVocational high schools offer a particularly revealing lens for this investigation. These institutions predominantly serve students from lower socioeconomic backgrounds who are more likely to enter the workforce directly and often engage in sexual relationships earlier than their academically tracked peers (Yu, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). With more than 16\u0026nbsp;million adolescents in vocational high schools, comprehensive sex education is not just an academic consideration but a critical developmental intervention for these adolescents' health, social outcomes, and transition to adulthood.\u003c/p\u003e \u003cp\u003eEmploying first-hand data from the baseline survey of a randomized controlled trial with 3,167 vocational high school students, this study aims to uncover the nuanced ways in which gender and geographical location influence adolescents' access to sex education. By mapping these intersectional inequalities, we contribute to a more sophisticated understanding of how educational disparities emerge and persist during adolescence, even within centralized systems which seemingly promote equity.\u003c/p\u003e \u003cp\u003eThis study makes three key contributions to the literature on adolescent development and educational inequality. We extend intersectionality theory by demonstrating how gender and geographical location interact to create unexpected patterns of educational access even within centralized systems which seemingly promote equality. This challenges conventional assumptions about how educational policies translate into practice during adolescent development. Empirically, we provide the first systematic evidence of how centralized educational policies create mechanisms that may produce uneven outcomes across different demographic groups during adolescence. Our focus on vocational schools offers unique insights into how educational inequality affects a crucial but understudied population of adolescents who often transition early into the workforce. Our analysis comparing regions at different development levels but under the same policy framework provides a novel approach to understanding how local implementation shapes adolescent educational experiences. This approach reveals patterns of inequality that might be missed in single-region studies.\u003c/p\u003e\n\u003ch3\u003eSchool-based Sex Education in China\u003c/h3\u003e\n\u003cp\u003eSchool-based sex education in China has evolved through various curricular frameworks, from \"puberty physiology\" and \"sexual morality\" to broader \"health education.\" The discourse predominantly centers on physiological development and risk prevention, framed through narratives of abstinence, social control, and moral conduct (Liu \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Liu \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pan \u0026amp; Huang 2011). This approach reflects broader societal anxieties about adolescent sexuality, with adults viewing it as a Pandora's box requiring careful management during this developmental period.\u003c/p\u003e \u003cp\u003eWithin China's education system, sex education remains marginally integrated into the formal curriculum during critical adolescent years. While the Ministry of Education's Guidelines for Health Education encourage incorporating sex education across subjects like Physical Education and Biology (Ministry of Education, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), implementation faces significant challenges: ambiguous incorporation strategies, minimal allocated hours, and inadequate professional training for educators (Gou et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite China's centralized educational system suggesting uniformity, these implementation gaps create significant variability in how adolescents access sex education, potentially reinforcing existing social inequalities during this formative stage.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGeographical Location and Adolescent Educational Inequality\u003c/h2\u003e \u003cp\u003eIn China, adolescents' access to educational resources varies dramatically by geographical location, offering insights into how regional development shapes youth opportunities. Guangdong Province exemplifies how economic development can create distinct educational environments: its history of international trade and economic reforms has established it as a hub of economic and cultural dynamism (Yin, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Vogel, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). In contrast, Yunnan Province presents significant developmental challenges, with limited infrastructure and restricted external influences (Zhang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These structural differences are reflected in development indicators: Yunnan ranks 27th out of 31 mainland provinces in the Human Development Index, while Guangdong stands at 6th, with GDP per capita nearly double that of Yunnan (Global Data Lab, 2019; National Bureau of Statistics, 2022).\u003c/p\u003e \u003cp\u003eThese provincial disparities shape adolescent sex education delivery. Guangdong has promoted comprehensive sexuality education emphasizing gender equality and adolescent development, particularly in advanced cities like Guangzhou and Shenzhen (Guangdong Education Committee, 2018). Conversely, Yunnan's approach remains conservative, prioritizing abstinence-focused \"Life, Survival, Living\" education (UNESCO \u0026amp; UNFPA, 2019).\u003c/p\u003e \u003cp\u003eThe urban-rural divide further compounds these developmental inequities. Urban students consistently receive more comprehensive sex education than their rural counterparts (China Family Planning Association, 2016). Research by Peking University and the Rural Women Development Foundation Guangdong (2018) reveals that rural adolescents face particular challenges: sex education often narrowly focuses on behavior prevention, lacking professional resources and comprehensive guidance. This creates a problematic developmental environment where adolescents encounter misleading health information while lacking structured education and supportive adult guidance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGender Ideology and Gendered Adolescent Sex Education\u003c/h3\u003e\n\u003cp\u003eGender inequalities persist in Chinese society, with deeply rooted cultural expectations shaping different socialization processes for girls and boys (Attan\u0026eacute;, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zuo et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These gender norms profoundly influence adolescent sex education, affecting what adults believe adolescents should learn based on their gender. Research reveals distinct patterns in how sexual information reaches adolescents: girls typically receive more structured education from parents and schools, while boys predominantly learn through mass media (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The gendered approach manifests in educational practices, with many schools initiating girls' sex education earlier, citing puberty onset (UNESCO \u0026amp; UNFPA, 2019), and implementing gender-segregated instruction. This creates problematic developmental narratives: boys are often framed as potential aggressors, while girls are taught defensive strategies and self-protection (Shi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nack, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Boyd, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese educational practices reinforce sexual double standards during adolescence. Female virginity receives disproportionate emphasis, reflecting moral judgments that differently evaluate sexual behavior based on gender (Sagebin et al., 2013; Zhang \u0026amp; Rao, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Some institutions even implement specialized \"girls' education\" programs promoting traditional femininity through activities like flower arrangement (Deng, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), further entrenching gender-based developmental trajectories.\u003c/p\u003e\n\u003ch3\u003eGaps in Past Literature\u003c/h3\u003e\n\u003cp\u003eResearch on adolescent sex education has focused primarily on measuring outcomes - knowledge, attitudes, and behaviors - across geographical locations and biological sexes (George, 2020; Liang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mbadu Muanda, 2018). This approach leaves critical developmental questions unexplored: Do differences in adolescent outcomes stem from varied educational exposure or other factors? The challenge of distinguishing between school-based education and alternative information sources further obscures how educational institutions may reproduce developmental inequalities during adolescence.\u003c/p\u003e \u003cp\u003eMost studies examine sex education within single regions (Chen \u0026amp; Wu, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fonner et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), limiting our understanding of how different contexts shape adolescent experiences. Particularly lacking is research examining the intersectionality between geographical location and gender in adolescent sex education access and delivery.\u003c/p\u003e \u003cp\u003eMoreover, vocational high school students remain understudied despite representing a crucial adolescent population. These institutions primarily serve youth from lower socioeconomic backgrounds who may face unique developmental challenges and earlier sexual debut (Yu, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This oversight leaves significant gaps in understanding how educational disparities affect adolescent development among more vulnerable populations, particularly in settings where early workforce entry is common.\u003c/p\u003e\n\u003ch3\u003eThis Study\u003c/h3\u003e\n\u003cp\u003eThis study examines how gender and geographical location interact to shape adolescents' exposure to sex education within a centralized educational system, focusing on vocational high schools in China's Guangdong and Yunnan provinces. This comparison of regions at different development levels, but operating under the same national policies, offers insights into how local implementation may create educational disparities during adolescence. Drawing on theoretical frameworks of social reproduction and intersectionality, we examine how these factors shape adolescent development opportunities through the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003ea: Rural adolescents will demonstrate significantly lower levels of school-based sex education exposure compared to their urban counterparts.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003eb: Adolescents in Guangdong province will exhibit higher levels of school-based sex education exposure relative to those in Yunnan province.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eMale adolescents will report lower levels of school-based sex education exposure compared to female adolescents.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eThe intersectionality of gender and geographical location will reveal a stratified pattern of sex education exposure during adolescence, with girls in Guangdong province experiencing the most comprehensive education, while boys in Yunnan province encounter the most limited interventions.\u003c/p\u003e \u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThe data for this study were obtained from the baseline survey of a randomized controlled trial evaluating the effectiveness of an online comprehensive sexuality education module in enhancing students' knowledge, attitudes, and behaviors related to sexual and reproductive health. A total of 3,358 adolescents from 28 schools participated in the study, completing the survey in April 2019. The sample included 10 schools from Guangdong province and 18 from Yunnan province. The schools varied in size, ranging from approximately 400 to 18,000 students, with most having more than 10 classes. From each school, two to four classes (approximately 100 students) were randomly selected from a list of 10th-grade classes. [Anonymized]. Missing data accounted for 5.69% of the baseline sample and were dropped list-wise.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of Variables\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDependent Variables\u003c/h2\u003e \u003cp\u003eStudents' exposure to sex education is measured along two dimensions: (1) the educational stages in which students were exposed to sex education, and (2) the specific topics within sex education that students have received.\u003c/p\u003e \u003cp\u003eThe stages in which students are exposed to sex education are measured using the question: \"At what stage have you received sex education systematically? [Multiple categories can be chosen].\" The response categories for this question include \"In primary school,\" \"In secondary school,\" and \"In vocational high school.\" Each response category is considered as one stage. The variable is coded as follows: 1\u0026thinsp;=\u0026thinsp;had sex education in none of the stages, 2\u0026thinsp;=\u0026thinsp;had sex education in some of the stages, and 3\u0026thinsp;=\u0026thinsp;had sex education in all three stages.\u003c/p\u003e \u003cp\u003eThe topics of sex education that students have received were measured using a variable constructed for students who have received any kind of sex education. Students who have never received any kind of sex education are coded as missing. The variable is measured by the question: \"From the first time you started receiving sex education to the present, which of the following sex education have you received? [Multiple categories can be chosen].\" The response categories for this question include \"Gender,\" \"Reproductive system,\" \"Puberty,\" \"Pregnancy and contraception,\" \"Abortion,\" \"STI and HIV/AIDS,\" \"Sexual behavior,\" \"Sexual violence,\" and \"Love and marriage.\" Each response category is coded as a binary variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Variables\u003c/h2\u003e \u003cp\u003eThe independent variables in this study include province, household registration, and students\u0026rsquo; biological sex. Biological sex is measured using the question: \"What is your biological sex?\" The response categories for this question are \"Female\" and \"Male.\" Girls are coded as 1, and boys are coded as 0. Biological sex was analyzed in the paper as the data do not include information on students' gender identity. Gender identity and gender diversity are not yet prevalent concepts in China, and students' sex education experiences largely align with their biological sex, thus their perceived gender by teachers. Province and household registration are two aspects that likely determine where students have been attending schools. Province is a dummy variable created based on the data collection location. The more economically developed Guangdong province is coded as 1, and the less economically developed Yunnan province is coded as 0. Household registration is an urban-rural dummy variable, and is created based on the question: \"Your household registration belongs to...\" The response categories for this question include \"local city or town,\" \"local rural area,\" \"other city or town,\" \"other rural area,\" and \"I don't know.\" The dummy variable is created such that \"local city or town\" and \"other city or town\" are coded as 1, and \"local rural area\" and \"other rural area\" are coded as 0. Household registration serves as a proxy for determining whether the student is from an urban or rural area prior to attending vocational high school. Province and household registration do not overlap with each other.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eIndividual baseline demographic variables, including ethnicity and average monthly living expenses, were included as covariates in the model. Average monthly living expenses serve as a proxy for the socioeconomic situation of the students' families.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cp\u003eIn this study, student observations are nested within classes and schools. Therefore, the three levels will be accounted for in the model.\u003c/p\u003e \u003cp\u003eThe number of stages at which students are exposed to sex education is analyzed using three-level random-intercept multinomial logistic regression with fixed slopes, using a sample size of 3187. The predicted probabilities of having sex education by stages were then calculated based on the regression output.\u003c/p\u003e \u003cp\u003eNext, a subsample of students who have received any form of sex education (N\u0026thinsp;=\u0026thinsp;1642) is included for the analysis of their exposure to nine sex education topics. The topics that students have received are analyzed using a three-level random-intercept binary logistic regression with fixed slopes. The equations for the nine topic models have the same structure\u003csup\u003e1\u003c/sup\u003e. The predicted probabilities of being exposed to different topics within sex education were then calculated based on the regression output.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive Results\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics of the study sample. Among the sample, 45.94% are girls, and slightly over two-thirds are from Yunnan province. Nearly 80% of the sample self-identify as having a birth registration in a rural area, while slightly less than a quarter of the sample belong to ethnic minorities.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Results of the Study Sample\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eFor All Students (N\u0026thinsp;=\u0026thinsp;3187)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiological Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvince\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuangdong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYunnan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Registration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinorities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving Expense (Mean(SD)) (100 CNY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStages being exposed to sex education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone of the stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eFor Students who have Received Sex Education (N\u0026thinsp;=\u0026thinsp;1642)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaving received the following content:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePuberty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTI and HIV/AIDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReproductive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSexual behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePregnancy and contraception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSexual violence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLove and marriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbortion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eRegarding exposure to sex education, almost half of the sample (48.48%) reported that they had never received any form of sex education. Another 46.25% of students indicated that they had received sex education in either primary, secondary, or high school, or in two of these stages. Only 5.27% of students reported receiving sex education in all three stages (primary, secondary, and high school). Among students who had received sex education, the most commonly taught topic was puberty, with 90.13% of students reporting that they had received education on this subject. Additionally, 81.30%, 75.94%, and 72.59% of students indicated that they had received sex education on STI and HIV/AIDS, the reproductive system, and gender, respectively. However, only 60.41% of students had received sex education on sexual behavior. Furthermore, only 48.17%, 30.09%, 29.66%, and 23.57% of students reported receiving sex education on pregnancy and contraception, sexual violence, love and marriage, and abortion, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eBivariate and Multivariate Results\u003c/h2\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003eNumber of Stages\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of bivariate and multivariate multinomial logistic regression analyses for the stages at which students were exposed to sex education. In the first panel (Some of the Stages vs. None of the Stages), the bivariate analysis shows that living expense has a statistically significant but very small bivariate association with students\u0026apos; likelihood of having sex education in some stages compared to students who have had no exposure to sex education. In the second panel (All of the Stages vs. None of the Stages), when the variables are analyzed individually, girls, students in Guangdong province, and students belonging to the ethnic majority (the Han ethnicity) have higher odds of having sex education in all stages rather than no sex education.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBivariate and Multivariate Multinomial Logistic Regression Models for Stages that Students were Exposed to Sex Education (N\u0026thinsp;=\u0026thinsp;3187)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome of the Stages vs. None of the Stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.88;1.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.89;1.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.90;1.27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.75;1.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuangdong Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.97;1.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.97;1.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.99;2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.80;1.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGirlXGuangdong Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.04;2.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.86;1.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.86;1.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.84;1.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.85;1.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.77;1.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.75;1.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.74;1.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving Expense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.00;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.00;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.00;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.66;1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.58;0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.61;1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll of the Stages vs. None of the Stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.02***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.43;2.85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.43;2.87]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.42;2.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.70;2.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuangdong Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.09***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.09***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.65***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.37***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[3.21;8.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[3.21;8.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[3.32;9.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.72;6.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGirlXGuangdong Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.27;5.49]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.49*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.98;2.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.05;2.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.01;2.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[1.04;2.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.32;0.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.60;1.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.59;1.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving Expense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.94;1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.99;1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.99;1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.02;0.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.01;0.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.02;0.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eModel 1 explores how sex, province, and urban-rural location are related to students\u0026apos; odds of having sex education. Model 2 adds control variables, including ethnicity and living expense. Interactions between significant variables in Model 2 were added to Model 2, and only the interaction between sex and province was found to be significant. Therefore, Model 3 includes the interaction between sex and province.\u003c/p\u003e\n \u003cp\u003eBased on Model 3, the interaction between biological sex and province is significantly related to students\u0026apos; odds of having sex education in some stages (OR\u0026thinsp;=\u0026thinsp;1.51; CI=[1.04;2.18]) or all stages (OR\u0026thinsp;=\u0026thinsp;2.64; CI=[1.27;5.49]). This shows that the effect of province on students\u0026rsquo; odds of having sex education depends on students\u0026rsquo; biological sex. Urban students also have higher odds of being exposed to sex education in all three stages compared to their rural counterparts (OR\u0026thinsp;=\u0026thinsp;1.53; CI=[1.04;2.26]).\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the breakdown of how biological sex, province, and location influence students\u0026apos; predicted probabilities of being exposed to sex education at the three stages. Students in Yunnan province consistently have a higher predicted probability of having no sex education in any of the stages compared to students in Guangdong province. In Guangdong, boys have a higher probability of having no sex education in any of the stages compared to girls. However, there is no substantial difference between boys and girls in Yunnan in terms of having no sex education in any of the stages. There are small differences in the probability of having sex education in some of the stages among different groups. Among students who have had sex education in all stages, the rural-urban difference becomes more pronounced. Students with a household registration in urban areas consistently have a higher probability of having received sex education in all three stages compared to their counterparts in rural areas. Additionally, girls in Guangdong province have significantly higher probabilities than boys from Guangdong province of being exposed to sex education in all three stages. However, the difference between girls and boys in Yunnan province is small. Overall, students in Guangdong province have significantly higher probabilities of having sex education in all stages compared to their counterparts from Yunnan province.\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePredicted Probabilities of Having Sex Education by Stages\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eGuangdong (N\u0026thinsp;=\u0026thinsp;1022)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eYunnan (N\u0026thinsp;=\u0026thinsp;2165)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGirls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBoys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGirls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBoys\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eExposure to Sex Education Topics for Students Who have had Sex Education\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the bivariate and multivariate binary logistic regression models for the exposure to nine different sex education topics among students who have received any kind of sex education before. Model 1 examines the influence of biological sex, province, and location on the exposure to different sex education topics. Model 2 adds control variables, including ethnicity and living expense. Subsequently, interactions between variables that were significant in Model 2 were tested. However, none of the interactions were found to be significant.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBivariate and Multivariate Binary Logistic Regression Models for Exposure to Topics of Sex Education (N\u0026thinsp;=\u0026thinsp;1642)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePuberty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSTI and HIV/AIDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eReproductive system\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.37;3.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.38;2.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.24;2.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.23;2.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.86;1.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.85;1.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuangdong Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.61***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[2.00;6.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.70;5.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.72;1.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.69;1.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;2.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.93;1.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.87;2.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.87;2.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.87;1.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.86;1.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.33;2.55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.31;2.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.33;0.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.41;0.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.60;1.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.62;1.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.56;1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.58;1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving Expense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.97;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99;1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99;1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[3.45;8.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[2.09;4.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.72;3.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSexual behavior\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePregnancy and contraception\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.10;1.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.09;1.77]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.30;2.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.29;2.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.43;2.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.45;2.34]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuangdong Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.90;1.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.78;1.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.97;2.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.94;2.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.57;1.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.60;1.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.95;1.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.97;1.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99;1.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;1.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.90;1.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.86;1.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.53;0.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.55;0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.69;1.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.73;1.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.75;1.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.75;1.34]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving Expense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0/97;1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.97;1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.01;1.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.01;1.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.71;3.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.61;1.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.32;0.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSexual violence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLove and marriage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAbortion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.21;1,96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.20;1.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.82;1.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.82;1.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.66;2.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.66;2.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuangdong Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;2.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.94;2.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.65;1.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.63;1.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.84;2.84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.80;2.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.01;1.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.02;1.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.07;1.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.04;1.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.83;1.55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.83;1.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.69;1.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.74;1.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.62;1.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.61;1.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.60;1.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.63;1.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving Expense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98;1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.18;0.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.25;0.49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.07;0.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, biological sex appears to be a significant factor in predicting the odds of seven out of nine sex education topics. Girls have higher odds of receiving sex education on puberty (OR\u0026thinsp;=\u0026thinsp;2.03, CI=[1.38;1.98]), STI and HIV/AIDS (OR\u0026thinsp;=\u0026thinsp;1.63; CI=[1.23;2.15]), gender (OR\u0026thinsp;=\u0026thinsp;1.39, CI=[1.09;1.77]), sexual behavior (OR\u0026thinsp;=\u0026thinsp;1.65; CI=[1.29;2.11]), pregnancy and contraception (OR\u0026thinsp;=\u0026thinsp;1.84, CI=[1.45;2.34]), sexual violence (OR\u0026thinsp;=\u0026thinsp;1.53; CI=[1.20;2.95]), and abortion (OR\u0026thinsp;=\u0026thinsp;2.21, CI=[1,66;2.94]) compared to their male counterparts. In one of the nine topics, province is a significant predictor. Students from Guangdong province have higher odds of receiving sex education on puberty (OR\u0026thinsp;=\u0026thinsp;2.95, CI=[1.70;5.12]) compared to their counterparts in Yunnan province. The urban-rural dummy variable appears to be significant in three of the nine topics. Having a birth registration in urban areas increases the odds of students receiving sex education on reproductive system (OR\u0026thinsp;=\u0026thinsp;1.82, CI=[1.31;2.54]), sexual violence (OR\u0026thinsp;=\u0026thinsp;1.34; CI=[1.02;1.75]), and love and marriage (OR\u0026thinsp;=\u0026thinsp;1.37; CI=[1.04;1.79]). Ethnicity appears to be significant in two of the models. The results show that students of minority ethnicity have lower odds of receiving sex education on puberty (OR\u0026thinsp;=\u0026thinsp;0.60, CI=[0.41;0.88]) and gender (OR\u0026thinsp;=\u0026thinsp;0.74, CI=[0.55;0.98]).\u003c/p\u003e\n \u003cp\u003eIn Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, we provide an overview of how sex, province, and urban-rural location influence students\u0026apos; likelihood of being exposed to different topics in sex education. Only factors that were found to be significant in the multivariate analyses are included in the predictions.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePredicted Probabilities of Having Sex Education on Different Topics (N\u0026thinsp;=\u0026thinsp;1642)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003eTopic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eSignificant Predictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003ePuberty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e93.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eBoy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e87.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eProvince\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGuangdong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e96.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eYunnan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e87.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003eSTI and HIV/AIDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e84.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eBoy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e77.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003eReproductive System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e82.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e73.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e75.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eBoy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e69.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003eSexual Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e64.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eBoy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e53.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003ePregnancy and Contraception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e53.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eBoy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e39.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003eSexual Violence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e33.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eBoy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e25.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e34.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e28.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003eLove and Marriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e33.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e27.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\n \u003cp\u003eAbortion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eGirl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e27.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eBoy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e15.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eThe findings suggest that students who have received any form of sex education generally have a lower chance of being taught topics related to pregnancy and contraception, abortion, sexual violence, and love and marriage. However, they have a higher chance of being taught topics on gender, reproductive system, puberty, and STI and HIV/AIDS. Students have a moderate chance of being taught about sexual behavior.\u003c/p\u003e\n \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, girls consistently have a significantly higher probability of being introduced to various topics in sex education compared to boys. Specifically, girls have a higher probability of being taught about puberty (93.13% vs. 87.31%), STI and HIV/AIDS (84.31% vs. 77.03%), gender (75.38% vs. 69.06%), sexual behavior (64.28% vs. 53.39%), pregnancy and contraception (53.22% vs. 39.73%), sexual violence (33.83% vs. 25.44%), and abortion (27.34% vs. 15.59%). The difference in the probabilities for girls and boys in terms of receiving education on pregnancy and contraception, abortion, and sexual behavior exceeds 10%, which is larger compared to other topics. Additionally, students with a household registration in urban areas also have a higher chance of receiving sex education on reproductive system (82.96% vs. 73.19%), sexual violence (33.83% vs. 25.44%), and love and marriage (33.59% vs. 27.27%) compared to their rural counterparts. Moreover, students in Guangdong province have a higher chance of receiving sex education on puberty (96.20% vs. 87.39%) compared to students in Yunnan province. However, urban-rural location and province are not as significant as gender in influencing the types of topics received.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study extends our understanding of how educational institutions reproduce social inequalities during adolescence, even within centralized systems which seemingly promote equity. By examining the intersection of gender and geographical location in Chinese vocational schools, we reveal systematic patterns of educational inequality that may significantly impact adolescent development trajectories. Our findings demonstrate that despite standardized national guidelines, access to crucial health education during adolescence remains stratified along multiple dimensions, with particularly pronounced effects for students in less developed regions and significant gender-based disparities in exposure to essential knowledge.\u003c/p\u003e \u003cp\u003eOur results extend social reproduction theory by demonstrating how geographical advantages compound with gender to create distinct educational trajectories during adolescence. Adolescents in the economically developed Guangdong province and those with urban registrations show higher odds of receiving sex education across multiple school stages. This pattern reveals how regional economic disparities translate into educational inequalities even within a highly unified policy framework. The intersectionality of these disparities is particularly evident among girls in Guangdong province, who show significantly higher odds of receiving sex education compared to all other groups.\u003c/p\u003e \u003cp\u003eResearch demonstrates that effective sex education during adolescence enhances sexual knowledge, attitudes, and risk-reducing behaviors (Kedzior et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wadham et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fonner et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kirby et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), while promoting healthy relationship development (Goldfarb et al., 2021). Given the importance of early and sequential sex education during adolescent development (Goldfarb et al., 2021), uneven access may amplify existing inequalities between developed and less developed regions (Yang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), affecting both health knowledge and overall well-being (Harris, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Domina et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These disparities create a cycle of developmental disadvantage that extends beyond education. Limited sexual health knowledge during adolescence increases risks of unintended pregnancies and early school departure, restricting future opportunities. In underdeveloped areas, the absence of comprehensive sex education perpetuates harmful gender norms and gender-based violence, particularly affecting adolescent girls' developmental trajectories.\u003c/p\u003e \u003cp\u003eWhile geographical location influences adolescents' access to sex education across developmental stages, students' sex, thus their perceived gender by teachers, emerges as the primary determinant of exposure to specific topics. Despite China's predominantly co-educational system, girls consistently show higher exposure across almost all topics. The disparity is particularly pronounced in topics such as pregnancy and contraception, abortion, and sexual behavior, where the difference in predicted probabilities between boys and girls exceeds 10%. This pattern suggests that sex education content during adolescence is primarily directed toward girls, with boys often excluded from crucial developmental discussions.\u003c/p\u003e \u003cp\u003eThese findings reflect broader societal gender expectations, where girls are positioned as responsible for \"refusing boys' sexual advances\" and \"protecting themselves\" (Shi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nack, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Boyd, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, this gendered approach carries significant developmental consequences. It places an unfair burden on girls regarding sexual responsibility, subjects them to harsher moral judgments when these expectations aren't met (Zhang \u0026amp; Rao, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and reinforces a protective framework that ultimately limits women's ability to compete with men (Rury, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). The emphasis on traditional gender roles and the construction of femininity and masculinity during this formative period may also contribute to increased rates of family violence later in life (Mshweshwe, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relative exclusion of boys from comprehensive sex education during adolescence has troubling developmental implications. Given that boys are more likely to engage in early sexual debut and risky behaviors (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), limited access to sex education may compromise their healthy development. This concern extends to vulnerable populations - since 2010, men who have sex with men have shown the highest HIV prevalence (Wu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with significant rates of intimate partner violence (Wei et al., 2019). Research indicates that mixed-gender approaches to sex education, particularly during adolescence, yield better developmental outcomes by facilitating direct communication between boys and girls (Pacifici et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Clinton-Sherrod et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Felty et al., 1991).\u003c/p\u003e \u003cp\u003eOur research also reveals concerning gaps in adolescent health education that have significant implications for development theory and practice. The finding that only 5% of students receive continuous sex education across all developmental stages challenges assumptions about the standardizing effects of centralized education systems. This discontinuity in health education during critical developmental periods may have lasting implications for adolescent health outcomes and social development, particularly affecting students from less advantaged backgrounds who may lack alternative sources of health information (UNFPA, 2018).\u003c/p\u003e \u003cp\u003eThis study's focus on vocational high schools illuminates how educational inequalities affect a vulnerable adolescent population during their transition to adulthood. Our findings demonstrate how intersectionality theory can reveal unexpected patterns of advantage and disadvantage within uniform educational systems, while suggesting practical implications for policy and practice. The significant regional variations and gender-based patterns in educational access indicate a need for targeted interventions and additional resources in less developed areas, alongside reconsidering how health education is delivered to ensure comprehensive coverage for both boys and girls.\u003c/p\u003e \u003cp\u003eThis study has two main limitations. First, as the data come from the baseline survey of a randomized controlled trial, representativeness may be limited (Matthews, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). While we included adolescents from both urban and rural settings across provinces of varying development levels, the schools were primarily from highly developed cities in each province. Future research should seek more representative samples and explore comparisons with academic high schools, where students' privileged backgrounds may not necessarily translate to better sex education access due to exam-focused curricula. Second, although 72.95% of students receiving sex education reported learning about gender, whether the content reinforces traditional roles or promotes equality and diversity remains unclear (UNFPA, 2008). Future studies should examine these nuances within gender education and their impacts on adolescent development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMoving beyond simplistic narratives about educational access, this study is the first to demonstrate how existing inequalities along gender and geographical lines are reproduced through educational institutions during adolescence, even within a centralized system which seemingly promotes equity. By examining the intersection of geographical location and gender in Chinese vocational schools, we reveal systematic patterns of educational inequality that may significantly impact adolescent development trajectories. These findings suggest how local implementation of centralized policies can reproduce and potentially amplify existing social disparities, with implications for understanding educational inequality more broadly. The study reveals that despite standardized guidelines, access to crucial health education during adolescence remains stratified, particularly affecting students in less developed regions and creating gendered patterns of exposure to essential knowledge. These patterns suggest the need for targeted interventions that consider both geographical and gender-based barriers to educational access. The findings have transnational relevance for other centralized educational systems, whether at the national or local level, seeking to address similar inequalities in adolescent education.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, our research suggests two key interventions. First, additional resources should be allocated to vocational schools in less developed regions to ensure standardized implementation of health education curricula. Second, teacher training programs should address implicit gender biases that may influence content delivery during adolescence.\u003c/p\u003e \u003cp\u003eFuture research should examine how these educational inequalities influence long-term developmental outcomes across different geographical and gender groups, with particular attention to vulnerable adolescent populations who may face unique challenges in their transition to adulthood. Understanding these intersecting dimensions of inequality is crucial for developing effective policies that can better support adolescent development across diverse contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original trial, which data of the current study comes from, was approved by the [Anonymized] in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the data collection process of the original trial, written informed consent was obtained at both the institutional level from participating schools and the individual level from students' guardians before data collection began in March 2019. The consent covered participation, data usage, and permission to publish. All participants were fully informed about the purpose of the research, how their data would be used, any potential risks of participation, and that their anonymity would be strictly protected.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eMC participated in the original randomized controlled trial as the key research assistant, responsible for leading, designing and implementing the study, collecting and analyzing data, and writing reports. MC also contributed to the funding acquisition, conceptualization, analysis, and interpretation of the data for the current study within the scope of this manuscript, which utilized the baseline survey data from the randomized controlled trial. Additionally, MC authored the first draft of the manuscript and managed subsequent revisions. All listed author(s) have made substantial contributions to the manuscript and have agreed to the final submitted version.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe author would like to thank Nanyang Technological University (Grant No. 024273-00001) for supporting the analysis and submission of the current manuscript; Xi\u0026rsquo;an Guangyuan Sex Education Support Charity Centre (Grant No. MSICKCF20172020001), which funded the original randomized controlled trial; Professor Kun Tang, the Principal Investigator of the cluster randomized controlled trial, from Tsinghua University Vanke School of Public Health, for support during the design and execution of the original randomized controlled trial, in which the author participated as the key research assistant responsible for the project; Marie Stopes International China and Professor Kun Tang for sharing the data; and all volunteers and team members, especially Tong Xin and Xueli Qiu, who have altruistically contributed to the study. The author would like to thank the schools, teachers and students enrolled in the study for their support and trust. The author would like to thank Dr. Catherine Zimmer for her help and guidance on the statistical analyses conducted in the study and Dr Yong Cai and Dr. Kathleen Mullan Harris for their valuable feedback on the manuscript.The author acknowledges the use of Claude 3.5 Sonnet for language improvement purposes.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe author of the current paper does not own the data, but data may be available from the Principal Investigator of the original randomized controlled trial, Dr. Kun Tang ([email protected]), or Marie Stopes International China Office, another owner of the data on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAttan\u0026eacute; I (2012) Being a woman in China today: A demography of gender. 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Reproductive Health 15(1):1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;The equation for the three-level random-intercept binary logistic regression model is:\u003c/span\u003e\n \u003cdiv id=\"Par33\" class=\"Para\"\u003e\n \u003cdiv id=\"IEq1\" class=\"InlineEquation\"\u003e\n \u003cdiv class=\"mathinline\" id=\"FileID_IEq1\" name=\"EquationSource\"\u003e\n \u003cscript type=\"math/tex; mode=inline\"\u003e\\:\\text{l}\\text{o}\\text{g}\\text{i}\\text{t}\\left\\{\\text{Pr}\\left(\\text{y}\\text{ijk}=1|\\mathbf{x}\\text{ijk},\\:{{\\upzeta\\:}\\text{jk}}^{\\left(2\\right)},{{\\upzeta\\:}\\text{k}}^{\\left(3\\right)}\\right)\\right\\}=\\left({\\beta\\:}_{1}+\\:{{\\upzeta\\:}\\text{jk}}^{\\left(2\\right)}+\\:{{\\upzeta\\:}\\text{k}}^{\\left(3\\right)}\\right)+\\:{\\beta\\:}_{2}{x}_{2ijk}+\\dots\\:+\\:{\\beta\\:}_{6}{x}_{6ijk}\\:\u003c/script\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Par34\" class=\"Para\"\u003e\u003cimg 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intersectionality, gender, geographical location","lastPublishedDoi":"10.21203/rs.3.rs-6154894/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6154894/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEducational institutions worldwide face challenges in equitably distributing resources during adolescence, a critical period for identity formation and social development. While centralized educational systems ostensibly promote standardization, less is known about how local implementation may reproduce social inequalities during this crucial stage. Through the lens of intersectionality, this study examines how gender and geographical location interact to shape adolescents' access to health education in centralized systems, using Chinese vocational high schools as an illustrative case. Drawing on data from 3,167 adolescents across regions representing different development levels, we demonstrate how educational institutions may inadvertently perpetuate social stratification through differential access to sex education, even within highly centralized systems. Our findings reveal complex interactions between geographical advantages and gender. Students in more economically developed regions and urban areas show significantly higher odds of receiving comprehensive health education, with particularly pronounced effects for girls in developed regions. Moreover, gender emerges as the primary determinant of exposure to specific developmental content. Girls consistently show higher access across topics, particularly regarding pregnancy and contraception, abortion, and sexual behavior, creating systematic disparities in access to essential knowledge during adolescence. These findings extend theories of educational inequality by revealing how intersecting dimensions of advantage and disadvantage manifest during crucial developmental transitions, even when formal policies mandate equal access, offering insights for policymakers seeking to promote equitable education for adolescents across diverse contexts.\u003c/p\u003e","manuscriptTitle":"Mapping Intersectionality in Adolescent Educational Inequality: Gendered and Geographic Disparities in Access to Sex Education in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 12:26:56","doi":"10.21203/rs.3.rs-6154894/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":"685c4e23-c4c8-4a0f-b231-803cbdda6268","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50067697,"name":"Social science/Education"},{"id":50067698,"name":"Social science/Social policy"},{"id":50067699,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2025-06-25T08:54:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 12:26:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6154894","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6154894","identity":"rs-6154894","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}