ObjectiveThe differences in sexual knowledge, attitudes, behaviors, seeking behaviors for sex-related knowledge, and sexual and reproductive health (SRH) outcomes among only-child students and students with siblings in China, was examined for sex- and region- specific effects.Research Design and MethodsData on 49,569 students from the 2019 National College Student Survey on Sexual and Reproductive Health, conducted across 31 provinces in mainland China was utilized. Multivariable regression and stratified analyses were employed to analyze the differences in sexual and reproductive health between only-child students and students with siblings.ResultsOnly-child students reported higher sexual knowledge, more liberal sexual attitudes, and fewer adverse SRH outcomes compared to those with siblings. Results were found to be influenced by sex and hometown region after controlling for socio-economic factors, parent-child relationship, and sexuality education.ConclusionsFemale students with siblings who resided in rural regions were more likely to have poorer SRH compared to male only-child students who resided in urban regions. Comprehensive sexual education for students should aim to better include females and students from rural areas both offline and online, and public healthcare should offer subsidized consultations and contraceptives.
Background Risky sexual behavior (RSB), the most direct risk factor for sexually transmitted infections (STIs), is common among college students. Thus, identifying relevant risk factors and predicting RSB are important to intervene and prevent RSB among college students. Objective We aim to establish a predictive model for RSB among college students to facilitate timely intervention and the prevention of RSB to help limit STI contraction. Methods We included a total of 8794 heterosexual Chinese students who self-reported engaging in sexual intercourse from November 2019 to February 2020. We identified RSB among those students and attributed it to 4 dimensions: whether contraception was used, whether the contraceptive method was safe, whether students engaged in casual sex or sex with multiple partners, and integrated RSB (which combined the first 3 dimensions). Overall, 126 predictors were included in this study, including demographic characteristics, daily habits, physical and mental health, relationship status, sexual knowledge, sexual education, sexual attitude, and previous sexual experience. For each type of RSB, we compared 8 machine learning (ML) models: multiple logistic regression (MLR), naive Bayes (BYS), linear discriminant analysis (LDA), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), deep learning (DL), and the ensemble model. The optimal model for both RSB prediction and risk factor identification was selected based on a set of validation indicators. An MLR model was applied to investigate the association between RSB and identified risk factors through ML methods. Results In total, 5328 (60.59%) students were found to have previously engaged in RSB. Among them, 3682 (41.87%) did not use contraception every time they had sexual intercourse, 3602 (40.96%) had previously used an ineffective or unsafe contraceptive method, and 1157 (13.16%) had engaged in casual sex or sex with multiple partners. XGBoost achieved the optimal predictive performance on all 4 types of RSB, with the area under the receiver operator characteristic curve (AUROC) reaching 0.78, 0.72, 0.94, and 0.80 for contraceptive use, safe contraceptive method use, engagement in casual sex or with multiple partners, and integrated RSB, respectively. By ensuring the stability of various validation indicators, the 12 most predictive variables were then selected using XGBoost, including the participants’ relationship status, sexual knowledge, sexual attitude, and previous sexual experience. Through MLR, RSB was found to be significantly associated with less sexual knowledge, more liberal sexual attitudes, single relationship status, and increased sexual experience. Conclusions RSB is prevalent among college students. The XGBoost model is an effective approach to predict RSB and identify corresponding risk factors. This study presented an opportunity to promote sexual and reproductive health through ML models, which can help targeted interventions aimed at different subgroups and the precise surveillance and prevention of RSB among college students through risk probability prediction.
Androgynous tendencies and persistently low fertility rates have been observed in many countries, causing major social concerns. The theory of sexual selection suggests a possible mechanism between androgyny and decreased sexual activeness, as masculinity and femininity constitute an important aspect of reproductive strategies. This theory has also been proven by evolutionary and societal evidence. Therefore, we investigate the association between masculinity and femininity with sexual activeness, as well as the influence of gender-role conformity on the frequency of sexual intercourse through sexually selected traits among 42,492 Chinese youths. Sexual activeness was measured using sexual attitudes, experience, behaviors, and pleasure. Mediation analysis was employed to investigate the effects of sexually selected traits on the association between masculinity and femininity with sexual activeness, and gender-role conformity with the frequency of sexual intercourse. Low sexual activeness was found to be associated with low gender-role conformity. Our findings also suggest that physical attractiveness, sexual motivation, and interpersonal relationships may mediate the association between sexual activeness and gender-role conformity, supporting the males-compete/females-choose model.
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