Clustered direct sexual risk behaviors and adverse sex-related outcomes among sexually experienced college students in Eastern China: a multicenter cross-sectional study of eHealth literacy, HIV/STI knowledge, and childhood/adolescent sexual victimization

Clustered direct sexual risk behaviors and adverse sex-related outcomes among sexually experienced college students in Eastern China: a multicenter cross-sectional study of eHealth literacy, HIV/STI knowledge, and childhood/adolescent sexual victimization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clustered direct sexual risk behaviors and adverse sex-related outcomes among sexually experienced college students in Eastern China: a multicenter cross-sectional study of eHealth literacy, HIV/STI knowledge, and childhood/adolescent sexual victimization Qiufeng Liu, Qianxu Yang, Yee Yin Hoo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9146107/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Risky sexual behavior remains an important public health concern among university students, but the roles of digital health literacy and sexual health knowledge are still unclear in Chinese settings. We examined clustered direct sexual risk behaviors and adverse sex-related outcomes among sexually experienced college students in Eastern China, with particular attention to eHealth literacy, HIV/STI knowledge, sex education, and childhood/adolescent sexual victimization. Methods We analyzed 1,681 sexually experienced participants drawn from a multicenter online survey conducted in October 2025 through Wenjuanxing. Student recruiters disseminated the survey through university-related WeChat and QQ groups. Because recruitment relied on open dissemination through student networks, a conventional response rate could not be calculated. The primary outcome was clustered direct sexual risk behavior, defined as reporting at least 2 of 4 direct behaviors: no contraception at first sex, casual sex, multiple concurrent partners, and drug-facilitated sex. A secondary composite captured any adverse sex-related outcome (unintended pregnancy/impregnation, abortion, or self-reported STI diagnosis). eHealth literacy was measured with the 8-item eHEALS, and HIV/STI knowledge was scored using a prespecified 29-item answer key. Modified Poisson regression with robust variance estimated adjusted prevalence ratios (aPRs). Results Participants were predominantly male (60.8%) and had a mean age of 21.5 (SD 2.3) years. The mean eHEALS score was 28.2 (SD 7.1); the mean knowledge score was 16.3 (SD 3.7). No contraception at first sex (65.0%) and casual sex (45.6%) were common. Overall, 36.0% reported clustered direct sexual risk behaviors and 16.5% reported at least one adverse sex-related outcome. eHEALS showed excellent internal consistency (Cronbach's alpha = 0.93), whereas the 29-item knowledge index had limited internal consistency (KR-20 = 0.51). In the adjusted primary model, contact/coercive victimization was strongly associated with clustered direct sexual risk behaviors (aPR 1.56, 95% CI 1.31–1.85), as were male sex (aPR 1.26, 95% CI 1.08–1.46), current smoking (aPR 1.22, 95% CI 1.05–1.41), older current age (aPR 1.04 per year, 95% CI 1.00-1.07), and higher knowledge score (aPR 1.15 per 5 points, 95% CI 1.05–1.25). Intact family structure (aPR 0.78, 95% CI 0.67–0.91) and older age at sexual debut (aPR 0.93 per year, 95% CI 0.89–0.98) were protective. eHEALS was not independently associated with the primary outcome (aPR 0.96 per 5 points, 95% CI 0.92–1.01). In contrast, higher eHEALS was associated with a lower prevalence of any adverse sex-related outcome (aPR 0.90, 95% CI 0.84–0.97). Conclusions Among sexually experienced college students in Eastern China, clustered direct sexual risk behaviors were common and were most consistently associated with contact/coercive victimization, earlier sexual debut, smoking, and male sex. Higher eHealth literacy was not independently associated with the behavior-only primary outcome, but it was associated with lower prevalence of adverse sex-related outcomes. These cross-sectional findings suggest that campus sexual health programs may benefit from trauma-informed, skills-based, and gender-sensitive support rather than relying on information provision alone. college students sexual risk behavior eHealth literacy HIV knowledge sexual victimization trauma-informed care China Figures Figure 1 Figure 2 Plain English summary Many college students look for sexual health information online, but it is still unclear whether this helps them avoid risky sexual situations. In this study, we focused only on students who had already had vaginal or anal sex and asked two related questions: who reports multiple risky sexual behaviors, and who reports negative sexual health outcomes such as unintended pregnancy, abortion, or a sexually transmitted infection? We analyzed 1,681 sexually experienced students from an online multicenter survey in Eastern China. More than one third reported at least two direct sexual risk behaviors, and about one in six reported at least one adverse sexual health outcome. Students who had experienced contact or coercive sexual victimization during childhood or adolescence were at especially high risk. Earlier sexual debut, smoking, and being male were also linked to higher risk. Two findings were more complex than expected. First, students with higher self-rated eHealth literacy did not clearly report fewer clusters of risky sexual behaviors. Second, students with higher factual HIV/STI knowledge were not safer in the main behavior model. This pattern should not be interpreted to mean that knowledge causes risk. One possible explanation is reverse temporality: students with greater exposure to risk may later encounter more information, services, or online material. However, higher eHealth literacy was associated with fewer adverse sexual health outcomes, which suggests that digital health skills may help students respond to risks more effectively even if they do not prevent all risky behaviors. Overall, the findings suggest that campus sexual health programs may be strengthened by being trauma-informed, gender-sensitive, and practical in focus, rather than relying on information alone. Background Sexual and reproductive health remains a major public health concern for adolescents and young adults worldwide. The World Health Organization reports that more than 1 million curable sexually transmitted infections (STIs) are acquired every day among people aged 15–49 years, and condoms remain one of the most effective tools for preventing HIV, many other STIs, and unintended pregnancy when used correctly and consistently [ 1 , 2 ]. College students are a particularly important population for sexual health research because the transition into university life can involve new sexual relationships, changing peer norms, increased autonomy, and greater exposure to alcohol and online partner-seeking environments. In China, recent research among sexually experienced college students has shown that sexual risk and HIV-related service uptake do not align neatly. Higher HIV testing uptake has been reported among students with riskier sexual histories, and PrEP-eligible behaviors have been linked to limited HIV/sex-related knowledge, lack of sex education, forced sex experiences, and drug abuse [ 7 , 8 ]. At the same time, sexual health learning increasingly occurs in digital environments. eHealth literacy refers to the ability to seek, find, understand, appraise, and use health information from electronic sources [ 3 ]. Among Chinese undergraduates, the eHealth Literacy Scale (eHEALS) has demonstrated good reliability and acceptable construct validity, including support for a 3-factor structure and gender measurement invariance [ 4 ]. Previous work has also shown that eHEALS is associated with preventive and health-promoting behaviors among Chinese college students [ 5 ]. However, research linking eHEALS specifically to sexual risk behavior remains sparse, and recent reviews note that sexual and reproductive health literacy research in higher education is still limited and fragmented [ 6 ]. Another underexamined issue is childhood and adolescent sexual victimization. International literature indicates that sexual victimization can shape later health risks, agency, and academic functioning [ 11 ]. In student populations, this history may help explain why some individuals report clustering of sexual risk behaviors and downstream adverse outcomes even after standard sociodemographic adjustment. Against this background, we analyzed sexually experienced college students from a multicenter online survey in Eastern China. Our aims were to: (1) describe the prevalence of clustered direct sexual risk behaviors and adverse sex-related outcomes; (2) examine eHealth literacy and HIV/STI knowledge in this population; and (3) assess whether eHealth literacy, HIV/STI knowledge, sex education, and childhood/adolescent sexual victimization were associated with clustered direct sexual risk behaviors after adjustment for sociodemographic and behavioral covariates. We also explored whether the same factors related to adverse sex-related outcomes. Methods Study design, setting, and recruitment This study used a multicenter cross-sectional online survey design. Data were collected during October 2025 through the Wenjuanxing platform. Student recruiters disseminated the survey link through university-related WeChat and QQ groups. Eligible participants were current college or university students in China who could complete the questionnaire online and provide electronic informed consent before entering the survey. Because recruitment relied on open dissemination through student networks, a formal denominator and conventional response rate could not be calculated. Reporting of the web-based survey was informed by CHERRIES where applicable, and the manuscript follows the STROBE recommendations for cross-sectional studies. The parent survey contained 2,413 valid questionnaires after data cleaning. For the present paper, we restricted the analytic sample to 1,681 respondents who reported previous vaginal or anal intercourse. Recruitment in the parent survey was concentrated in Zhejiang (43.1%), Jiangsu (28.8%), and Shanghai (21.8%), with smaller contributions from Beijing (0.7%) and other provinces/regions (5.5%). The survey should therefore be interpreted as multicenter but regionally clustered in Eastern China rather than nationally representative. Data cleaning Questionnaires were screened before analysis for logic conflicts, contradictions, and implausible completion patterns. The cleaned export contained no standard missing values, but it did include a small number of implausible derived values. Five implausible birth dates and 14 invalid age-at-sexual-debut entries (11 skipped responses and 3 impossible numeric values) were corrected using prespecified deterministic median imputation within academic-stage or sex strata. No additional missing data remained in the final analytic dataset. Measures Behavioral and adverse outcome items were adapted from measures used in the National College Student Survey on Sexual and Reproductive Health in China [ 9 ]. We intentionally separated direct sexual risk behaviors from downstream adverse outcomes. The primary outcome was clustered direct sexual risk behavior, defined as reporting at least 2 of 4 direct behaviors: no contraception at first sex, casual sex, multiple concurrent partners, and drug-facilitated sex. A stricter sensitivity outcome used a threshold of at least 3 of these 4 behaviors. Secondary outcomes included each direct behavior separately, any adverse sex-related outcome (at least 1 of unintended pregnancy/impregnating someone, abortion, or self-reported STI diagnosis), and each adverse outcome separately. eHealth literacy was assessed with the 8-item eHEALS using 5 response options from strongly disagree to strongly agree (range 8–40), with higher scores indicating higher perceived eHealth literacy [ 3 – 5 ]. HIV/STI knowledge was measured using 29 true/false items adapted from the ARCSHS National Survey of Australian Secondary Students and Sexual Health and the RUSSL sexual health literacy study. Correct answers were scored using the prespecified investigator-provided answer key, producing a total score from 0 to 29. Because the items span multiple domains, including HIV transmission, STI symptoms, hepatitis, HPV, vaccination, and PrEP/PEP, we treated the knowledge score as a formative knowledge index rather than as a unidimensional reflective scale. Childhood/adolescent sexual victimization was categorized into 3 mutually exclusive groups: none, harassment only, and contact/coercive victimization. Harassment-only exposure included online or real-world verbal sexual harassment without physical contact. The contact/coercive category included forced exposure of breasts or genitals, nonconsensual touching of breasts or genitals, nonconsensual oral sex, and nonconsensual vaginal or anal intercourse. Sex education was measured as a binary self-report item asking whether the participant had ever received sex education. Covariates were sex, current age, institution type, relationship status, parental education, monthly expenditure, family structure, smoking, alcohol use, physical activity, and age at sexual debut. Statistical analysis We summarized categorical variables as counts and percentages and continuous variables as means and standard deviations. Internal consistency was evaluated with Cronbach's alpha for eHEALS and Kuder-Richardson Formula 20 (KR-20) for the 29-item knowledge index. The correlation between eHEALS and knowledge was assessed with Pearson correlation. Because the primary outcome was common, we used modified Poisson regression with robust variance to estimate adjusted prevalence ratios (aPRs), which are more interpretable than odds ratios when binary outcomes are not rare [ 12 ]. The primary multivariable model retained eHEALS a priori as a prespecified exposure of substantive interest. Sensitivity analyses included a model excluding eHEALS, a stricter primary outcome threshold ( > = 3 of 4 behaviors), an exploratory eHEALS quartile model, an exploratory domain-specific knowledge model, and a sex-by-victimization interaction model. Analyses were performed in Python 3.11, and two-sided P values < 0.05 were considered statistically significant. Because the data were cross-sectional, all multivariable estimates were interpreted as associational rather than causal. Results Sample characteristics and measurement properties The analytic sample comprised 1,681 sexually experienced students. Men accounted for 60.8% of participants. The mean approximate age was 21.5 (SD 2.3) years, and the mean age at sexual debut was 19.0 (SD 1.7) years after correction of a small number of invalid entries. The sample included 68.8% with no reported childhood/adolescent sexual victimization, 20.0% with harassment-only exposure, and 11.2% with contact/coercive victimization. eHEALS averaged 28.2 (SD 7.1), and the knowledge index averaged 16.3 (SD 3.7). Table 1 shows the sample characteristics by primary-outcome status. Table 1 Sample characteristics by clustered direct sexual risk behavior status Characteristic Total, n (%) =2 behaviors, n (%) Age, mean ± SD 21.5 ± 2.3 21.6 ± 2.3 21.5 ± 2.2 Age at sexual debut, mean ± SD 19.0 ± 1.7 19.1 ± 1.7 18.8 ± 1.7 eHEALS score, mean ± SD 28.2 ± 7.1 28.5 ± 6.9 27.7 ± 7.3 Knowledge score, mean ± SD 16.3 ± 3.7 16.1 ± 3.8 16.6 ± 3.6 Sex Male 1022 (60.8) 618 (57.5) 404 (66.7) Female 659 (39.2) 457 (42.5) 202 (33.3) Institution Regular university 1069 (63.6) 714 (66.4) 355 (58.6) 985 university 128 (7.6) 73 (6.8) 55 (9.1) 211 university 198 (11.8) 140 (13.0) 58 (9.6) Vocational college 286 (17.0) 148 (13.8) 138 (22.8) Relationship Never dated 35 (2.1) 25 (2.3) 10 (1.7) Currently in relationship 939 (55.9) 582 (54.1) 357 (58.9) Previously dated, now separated 707 (42.1) 468 (43.5) 239 (39.4) Parentedu Junior high or below 276 (16.4) 172 (16.0) 104 (17.2) High school/secondary school 847 (50.4) 547 (50.9) 300 (49.5) Junior college 367 (21.8) 224 (20.8) 143 (23.6) Bachelor 167 (9.9) 117 (10.9) 50 (8.3) Postgraduate 24 (1.4) 15 (1.4) 9 (1.5) Spending =4000 RMB 63 (3.7) 37 (3.4) 26 (4.3) Family Intact family 1419 (84.4) 935 (87.0) 484 (79.9) Other family structure 262 (15.6) 140 (13.0) 122 (20.1) Smoking Never smoking 850 (50.6) 600 (55.8) 250 (41.3) Current smoking 831 (49.4) 475 (44.2) 356 (58.7) Alcohol Rare/none 1319 (78.5) 882 (82.0) 437 (72.1) Frequent alcohol use 362 (21.5) 193 (18.0) 169 (27.9) Sexedu Received sex education 957 (56.9) 614 (57.1) 343 (56.6) No sex education 724 (43.1) 461 (42.9) 263 (43.4) Victim None 1156 (68.8) 778 (72.4) 378 (62.4) Harassment only 336 (20.0) 214 (19.9) 122 (20.1) Contact/coercive 189 (11.2) 83 (7.7) 106 (17.5) Percentages in the outcome columns are column percentages. eHEALS showed excellent internal consistency (Cronbach's alpha = 0.93). In contrast, the 29-item knowledge index had limited internal consistency (KR-20 = 0.51), consistent with the heterogeneity of its item content. eHEALS and the knowledge index were only weakly correlated (r = 0.10, P < 0.001) (Table 3 ). Detailed item-level performance is provided in the Supplementary Materials. Prevalence of direct sexual risk behaviors and adverse outcomes Direct sexual risk behaviors were common. No contraception at first sex was reported by 65.0% of participants, casual sex by 45.6%, drug-facilitated sex by 18.4%, and multiple concurrent partners by 14.9%. Overall, 36.0% reported at least 2 of the 4 direct behavior items and 9.1% reported at least 3. Adverse sex-related outcomes were less frequent but still notable: 7.3% reported unintended pregnancy or impregnating someone, 10.7% reported abortion or causing a partner abortion, 2.7% reported a self-reported STI diagnosis, and 16.5% reported at least one adverse outcome. These prevalences are summarized in Table 2 and Fig. 1 . Table 2 Prevalence of direct sexual risk behaviors and adverse sex-related outcomes Outcome / behavior n % No contraception at first sex 1093 65.0 Casual sex 766 45.6 Multiple concurrent partners 250 14.9 Drug-facilitated sex 310 18.4 Clustered direct sexual risk behaviors ( > = 2 of 4) 606 36.0 Clustered direct sexual risk behaviors ( > = 3 of 4) 153 9.1 Any adverse sex-related outcome 277 16.5 Unintended pregnancy / impregnating someone 123 7.3 Abortion / causing a partner abortion 180 10.7 Self-reported STI diagnosis 45 2.7 Clustered direct sexual risk behavior was defined as reporting at least 2 of 4 direct behaviors. Table 3 Psychometric summary of eHEALS and the 29-item knowledge index Measure Statistic Value eHEALS total Cronbach alpha 0.93 29-item HIV/STI knowledge index KR-20 0.51 Pearson correlation between eHEALS and knowledge r (P value) 0.10 (P < 0.001) The knowledge measure was treated as a formative index because the item pool covered heterogeneous content domains. Primary multivariable model In the fully adjusted modified Poisson model for clustered direct sexual risk behaviors, contact/coercive victimization was the strongest and most consistent correlate (aPR 1.56, 95% CI 1.31–1.85). Male sex (aPR 1.26, 95% CI 1.08–1.46), current age (aPR 1.04 per year, 95% CI 1.00-1.07), current smoking (aPR 1.22, 95% CI 1.05–1.41), and higher knowledge score (aPR 1.15 per 5 points, 95% CI 1.05–1.25) were associated with a higher prevalence of clustered direct risk behaviors. Intact family structure (aPR 0.78, 95% CI 0.67–0.91) and older age at sexual debut (aPR 0.93 per year, 95% CI 0.89–0.98) were associated with a lower prevalence. Harassment-only exposure was borderline (aPR 1.17, 95% CI 1.00-1.38), and eHEALS was not independently associated with the primary outcome in the continuous model (aPR 0.96 per 5 points, 95% CI 0.92–1.01). Main model estimates are shown in Table 4 and Fig. 2 . Table 4 Modified Poisson regression for clustered direct sexual risk behaviors ( > = 2 of 4 behaviors) Predictor Crude PR (95% CI) Adjusted PR (95% CI) P value Harassment only (vs none) 1.11 (0.94–1.31) 1.17 (1.00-1.38) 0.054 Contact/coercive victimization (vs none) 1.72 (1.48–1.99) 1.56 (1.31–1.85) < 0.001 eHEALS (per 5-point increase) 0.95 (0.91–0.99) 0.96 (0.92–1.01) 0.098 Knowledge index (per 5-point increase) 1.12 (1.03–1.22) 1.15 (1.05–1.25) 0.002 Received sex education 0.99 (0.87–1.12) 1.00 (0.88–1.14) 0.972 Male sex 1.29 (1.12–1.48) 1.26 (1.08–1.46) 0.003 Current age (per year) 0.99 (0.97–1.02) 1.04 (1.00-1.07) 0.034 Intact family structure 0.73 (0.63–0.85) 0.78 (0.67–0.91) 0.001 Current smoking 1.46 (1.28–1.66) 1.22 (1.05–1.41) 0.008 Frequent alcohol use 1.41 (1.23–1.61) 1.14 (0.99–1.31) 0.060 Age at sexual debut (per year) 0.92 (0.89–0.96) 0.93 (0.89–0.98) 0.003 The adjusted model additionally included institution type, relationship status, parental education, monthly expenditure, and physical activity. Secondary and sensitivity analyses Secondary analyses revealed a more differentiated pattern. Higher eHEALS was associated with lower prevalence of no contraception at first sex (aPR 0.97, 95% CI 0.95-1.00) and lower prevalence of any adverse sex-related outcome (aPR 0.90, 95% CI 0.84–0.97), but it was not clearly associated with clustered direct sexual risk behaviors as a whole. Contact/coercive victimization was strongly associated with multiple concurrent partners (aPR 2.51, 95% CI 1.88–3.36), abortion (aPR 1.54, 95% CI 1.10–2.15), self-reported STI diagnosis (aPR 3.11, 95% CI 1.63–5.96), and any adverse sex-related outcome (aPR 1.69, 95% CI 1.31–2.17). Selected secondary models are provided in the Supplementary Materials. Sensitivity analyses did not materially change the main pattern of findings. Excluding eHEALS from the primary model left the estimates for victimization, knowledge, sex, smoking, and age at sexual debut largely unchanged. In the stricter > = 3-behavior model, contact/coercive victimization, frequent alcohol use, male sex, and earlier sexual debut remained important correlates. The exploratory eHEALS quartile model suggested a non-monotonic pattern, with lower adjusted prevalence in the second and third quartiles relative to the lowest quartile but no clear gradient in the highest quartile. In an exploratory domain-specific knowledge model, only the HIV knowledge domain was positively associated with the primary outcome, whereas STI/HPV and hepatitis subdomains were not. A sex-by-victimization interaction suggested that the association between contact/coercive victimization and clustered direct risk behaviors was stronger among women than among men. Discussion This cross-sectional study adds to the literature by focusing on heterogeneity within sexually experienced college students rather than by contrasting sexually active and sexually inactive groups. Three findings deserve emphasis. First, direct sexual risk behavior clustering was common, with more than one third of respondents reporting at least 2 direct behavior items. Second, childhood/adolescent contact or coercive victimization was the strongest and most consistent correlate of both clustered direct risk behaviors and downstream adverse outcomes. Third, the expected protective roles of eHEALS and factual HIV/STI knowledge were not supported in a simple linear way. The victimization findings are especially important for campus health policy and practice. Distinguishing harassment-only exposure from contact/coercive victimization improved interpretability. After adjustment, harassment-only exposure showed only limited association with the primary outcome, whereas contact/coercive victimization remained robustly associated with clustered risk behavior and multiple adverse outcomes. This pattern is consistent with the potential value of trauma-informed campus prevention, screening, and referral pathways. Our exploratory interaction analysis further suggested that the victimization-risk association may be stronger among women, even though the overall prevalence of clustered direct risk behavior was higher among men. That interpretation is consistent with gender-sensitive models in which men may have higher baseline behavioral exposure, while the downstream consequences of coercive trauma may be especially pronounced among women. The eHEALS findings were more nuanced than a simple protective-hypothesis model. eHEALS was not independently associated with the behavior-only primary outcome in the continuous model, but it was associated with lower prevalence of no contraception at first sex and lower prevalence of any adverse sex-related outcome. One plausible interpretation is that self-perceived digital health literacy may help students navigate consequences, information seeking, and help-seeking once risk emerges, even if it does not fully prevent clustering of direct behaviors. The weak correlation between eHEALS and the knowledge index also suggests that they capture related but distinct constructs: perceived digital self-efficacy versus retained factual sexual health content. This finding adds to ongoing discussion about how digital health literacy should be operationalized in sexual-health research among young adults in culturally specific settings [ 4 – 6 ]. The positive association between the knowledge index and the primary outcome should not be interpreted causally as evidence that knowledge increases risk. The effect size was modest, the cross-sectional design leaves reverse temporality plausible, and the exploratory domain model suggested that this pattern was driven mainly by the HIV knowledge subdomain rather than by STI/HPV or hepatitis content. Students with greater exposure to sexual risk may subsequently encounter HIV-related education, peer discussion, testing services, or online content and thus score higher on some knowledge items. Importantly, even after we rescored the 29 items using the prespecified investigator-provided answer key, KR-20 remained low. This indicates that the measurement problem is not simply one of miscoding; instead, the item pool appears to function as a heterogeneous knowledge index rather than as a coherent single latent scale. Sex education should also be interpreted cautiously in this study. Our measure captured only whether participants had ever received sex education. It did not distinguish timing, content, skill components, delivery format, or whether the exposure occurred before or after sexual debut. Accordingly, the absence of a protective association in the primary model, and even the positive associations seen in some secondary models, should not be read as evidence that sex education is ineffective. Rather, they show that a simple yes/no item is a poor proxy for the quality or timing of sexuality education. This study has several strengths. It focused on sexually experienced students, reported design and ethics information transparently, separated direct sexual behaviors from downstream adverse outcomes, used a documented answer key for the knowledge items, and estimated prevalence ratios rather than odds ratios for a common binary outcome. The study also included multiple sensitivity and exploratory analyses, including stricter outcome definitions, models excluding eHEALS, domain-specific knowledge models, and sex-by-victimization interaction analyses. Several limitations should be considered. First, the cross-sectional design precludes causal inference, and reverse temporality is plausible for associations involving eHealth literacy, knowledge, sex education, and adverse outcomes. Second, all measures were self-reported and may be affected by recall error and social desirability bias, particularly for sensitive sexual behaviors, abortion history, STI diagnosis, and victimization. Third, recruitment occurred through open dissemination in university-related WeChat and QQ groups, so a denominator and conventional response rate could not be calculated; sampling weights were unavailable, and selection bias is possible if students who were more connected to these networks or more willing to disclose sexual experiences were more likely to participate. Fourth, the sample was multicenter but regionally clustered in Eastern China and is not nationally representative. The sexually experienced analytic export did not retain institution identifiers, so the regional distribution is described using the parent survey frame. Fifth, several outcomes were lifetime-type cumulative measures and may therefore correlate with current age. Sixth, the knowledge index showed limited internal consistency and was treated as a heterogeneous formative index rather than a single latent scale, while the sex education measure was coarse and did not capture timing, dose, content, or quality. Finally, although only a small number of implausible age-related values required deterministic correction, and no additional missing data remained after cleaning, residual measurement error and unmeasured confounding cannot be excluded. Conclusions Among sexually experienced college students drawn from a multicenter online survey in Eastern China, clustered direct sexual risk behaviors were common and were most strongly associated with childhood/adolescent contact or coercive sexual victimization, earlier sexual debut, current smoking, and male sex. Higher eHealth literacy was not independently associated with the behavior-only primary outcome, but it was associated with lower prevalence of adverse sex-related outcomes. These cross-sectional findings suggest that universities may benefit from moving beyond information provision alone and considering trauma-informed, skills-based, and gender-sensitive sexual health support. Abbreviations aPR adjusted prevalence ratio CHERRIES Checklist for Reporting Results of Internet E-Surveys eHEALS eHealth Literacy Scale HIV human immunodeficiency virus HPV human papillomavirus KR-20 Kuder-Richardson Formula 20 PrEP pre-exposure prophylaxis PEP post-exposure prophylaxis STI sexually transmitted infection STROBE Strengthening the Reporting of Observational Studies in Epidemiology Declarations Ethics approval and consent to participate: The study protocol was approved by the Ethics Committee of Jiangsu College of Nursing (approval No. JSCN-ME-2025070719). All participants provided electronic informed consent before entering the online questionnaire. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests. Funding: This research received no external funding. Author Contribution Qiufeng Liu conceived the study, oversaw data collection, and supervised the project. Qianxu Yang conducted the statistical analysis, prepared the figures and tables, and drafted the manuscript. Yee Yin Hoo contributed to methodology, interpretation, and critical revision of the manuscript. All authors read and approved the final manuscript. Acknowledgement The authors thank the participating students and the student recruiters who assisted with questionnaire dissemination through university WeChat and QQ groups. The authors are also grateful to Taizhou University, Zhejiang University, Ningbo University, Wuxi University, Shanghai University of Electric Power, East China University of Science and Technology, Nanjing University, Shanghai Ocean University, and Wenzhou University for recruitment support and study dissemination. Data Availability The de-identified analytic dataset and statistical code that support the findings of this study are not publicly available because the survey addressed sensitive sexual health information and the ethics approval did not include unrestricted public release. They are available from the corresponding author on reasonable request, subject to approval by the Ethics Committee of Jiangsu College of Nursing. References World Health Organization. Sexually transmitted infections (STIs). Geneva: WHO; 2025. World. Health Organization. Condoms. Geneva: WHO; 2025. Norman CD, Skinner HA. eHEALS: The eHealth Literacy Scale. J Med Internet Res. 2006;8(4):e27. 10.2196/jmir.8.4.e27 . Long C, Zheng L, Liu R, Duan Z. Structural validation and measurement invariance testing of the Chinese version of the eHealth Literacy Scale among undergraduates: cross-sectional study. J Med Internet Res. 2023;25:e48838. 10.2196/48838 . Li S, Cui G, Kaminga AC, Cheng S, Xu H. Associations between health literacy, eHealth literacy, and COVID-19-related health behaviors among Chinese college students: cross-sectional online study. J Med Internet Res. 2021;23(5):e25600. 10.2196/25600 . Alhussaini NWZ, Elshaikh U, Abdulrashid K, Elashie S, Hamad NA, Al-Jayyousi GF. Sexual and reproductive health literacy of higher education students: a scoping review of determinants, screening tools, and effective interventions. Glob Health Action. 2025;18(1):2480417. 10.1080/16549716.2025.2480417 . He J, Cen P, Qin J, et al. Uptake of HIV testing and its correlates among sexually experienced college students in Southwestern China: a web-based cross-sectional study. BMC Public Health. 2023;23:1702. 10.1186/s12889-023-16638-z . Qin S, Qin J, Su Q, et al. HIV knowledge, sexual attitudes, and PrEP-eligible behaviors among college students in Southwest China: a cross-sectional study. BMC Infect Dis. 2024;24:827. 10.1186/s12879-024-09657-7 . Zhao S, Liang Y, Hee JY, Qi X, Tang K. 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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-9146107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622993634,"identity":"4621c3da-6866-4e08-a1d4-9621d3f0e858","order_by":0,"name":"Qiufeng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYBACPgbGBwwP2Gx4+PkbiNTCxsBswJDAliYjOeMAaVoO2xg0JBCrhT2Z+UNC2XkeA4YDjB8+5hCjhecxm0TCuds85swNzJIztxGjRSL/GENi220ey4YDbMy8xGkBOiyx7RyPwYEE4rUwSCS2HSBFC8QvyTySMw42E+cXflCIfSizs+fnbz744SMxWhgYEmAMxgai1CNrGQWjYBSMglGAAwAA3cwxUBsFg7sAAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu College of Nursing","correspondingAuthor":true,"prefix":"","firstName":"Qiufeng","middleName":"","lastName":"Liu","suffix":""},{"id":622993640,"identity":"f9d3992c-76fa-4ec4-9d96-7896ad762c9a","order_by":1,"name":"Qianxu Yang","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Qianxu","middleName":"","lastName":"Yang","suffix":""},{"id":622993644,"identity":"cee5c7b4-dd5e-4684-ba4a-89ecdfd50e4c","order_by":2,"name":"Yee Yin Hoo","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Yee","middleName":"Yin","lastName":"Hoo","suffix":""}],"badges":[],"createdAt":"2026-03-17 08:30:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9146107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9146107/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107500607,"identity":"b3e768e9-09e8-4777-aff8-3814c41f6fc2","added_by":"auto","created_at":"2026-04-22 05:48:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":251189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePrevalence of direct sexual risk behaviors and adverse sex-related outcomes\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9146107/v1/10ed9781e6c1367d1939afd6.png"},{"id":107705197,"identity":"7889008b-9dd7-40c9-aa82-d6646170cd7e","added_by":"auto","created_at":"2026-04-24 09:09:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":256973,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eForest plot of selected adjusted prevalence ratios for clustered direct sexual risk behaviors\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9146107/v1/1bf3d5becf44bb762d015c9c.png"},{"id":107712587,"identity":"7e78c50b-43a3-42b7-b60a-cc9eb474cad1","added_by":"auto","created_at":"2026-04-24 09:49:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":761253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9146107/v1/7d3a8c15-51b4-4b09-9ee9-7fa857e2de06.pdf"},{"id":107705576,"identity":"1159d5db-e7d8-43a1-b442-d2bbffb6e91d","added_by":"auto","created_at":"2026-04-24 09:13:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45174,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterialsfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-9146107/v1/9bf20250a206831df0c3282e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clustered direct sexual risk behaviors and adverse sex-related outcomes among sexually experienced college students in Eastern China: a multicenter cross-sectional study of eHealth literacy, HIV/STI knowledge, and childhood/adolescent sexual victimization","fulltext":[{"header":"Plain English summary","content":"\u003cp\u003eMany college students look for sexual health information online, but it is still unclear whether this helps them avoid risky sexual situations. In this study, we focused only on students who had already had vaginal or anal sex and asked two related questions: who reports multiple risky sexual behaviors, and who reports negative sexual health outcomes such as unintended pregnancy, abortion, or a sexually transmitted infection?\u003c/p\u003e\n\u003cp\u003eWe analyzed 1,681 sexually experienced students from an online multicenter survey in Eastern China. More than one third reported at least two direct sexual risk behaviors, and about one in six reported at least one adverse sexual health outcome. Students who had experienced contact or coercive sexual victimization during childhood or adolescence were at especially high risk. Earlier sexual debut, smoking, and being male were also linked to higher risk.\u003c/p\u003e\n\u003cp\u003eTwo findings were more complex than expected. First, students with higher self-rated eHealth literacy did not clearly report fewer clusters of risky sexual behaviors. Second, students with higher factual HIV/STI knowledge were not safer in the main behavior model. This pattern should not be interpreted to mean that knowledge causes risk. One possible explanation is reverse temporality: students with greater exposure to risk may later encounter more information, services, or online material. However, higher eHealth literacy was associated with fewer adverse sexual health outcomes, which suggests that digital health skills may help students respond to risks more effectively even if they do not prevent all risky behaviors.\u003c/p\u003e\n\u003cp\u003eOverall, the findings suggest that campus sexual health programs may be strengthened by being trauma-informed, gender-sensitive, and practical in focus, rather than relying on information alone.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eSexual and reproductive health remains a major public health concern for adolescents and young adults worldwide. The World Health Organization reports that more than 1\u0026nbsp;million curable sexually transmitted infections (STIs) are acquired every day among people aged 15\u0026ndash;49 years, and condoms remain one of the most effective tools for preventing HIV, many other STIs, and unintended pregnancy when used correctly and consistently [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCollege students are a particularly important population for sexual health research because the transition into university life can involve new sexual relationships, changing peer norms, increased autonomy, and greater exposure to alcohol and online partner-seeking environments. In China, recent research among sexually experienced college students has shown that sexual risk and HIV-related service uptake do not align neatly. Higher HIV testing uptake has been reported among students with riskier sexual histories, and PrEP-eligible behaviors have been linked to limited HIV/sex-related knowledge, lack of sex education, forced sex experiences, and drug abuse [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the same time, sexual health learning increasingly occurs in digital environments. eHealth literacy refers to the ability to seek, find, understand, appraise, and use health information from electronic sources [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among Chinese undergraduates, the eHealth Literacy Scale (eHEALS) has demonstrated good reliability and acceptable construct validity, including support for a 3-factor structure and gender measurement invariance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous work has also shown that eHEALS is associated with preventive and health-promoting behaviors among Chinese college students [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, research linking eHEALS specifically to sexual risk behavior remains sparse, and recent reviews note that sexual and reproductive health literacy research in higher education is still limited and fragmented [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother underexamined issue is childhood and adolescent sexual victimization. International literature indicates that sexual victimization can shape later health risks, agency, and academic functioning [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In student populations, this history may help explain why some individuals report clustering of sexual risk behaviors and downstream adverse outcomes even after standard sociodemographic adjustment.\u003c/p\u003e \u003cp\u003eAgainst this background, we analyzed sexually experienced college students from a multicenter online survey in Eastern China. Our aims were to: (1) describe the prevalence of clustered direct sexual risk behaviors and adverse sex-related outcomes; (2) examine eHealth literacy and HIV/STI knowledge in this population; and (3) assess whether eHealth literacy, HIV/STI knowledge, sex education, and childhood/adolescent sexual victimization were associated with clustered direct sexual risk behaviors after adjustment for sociodemographic and behavioral covariates. We also explored whether the same factors related to adverse sex-related outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, setting, and recruitment\u003c/h2\u003e \u003cp\u003eThis study used a multicenter cross-sectional online survey design. Data were collected during October 2025 through the Wenjuanxing platform. Student recruiters disseminated the survey link through university-related WeChat and QQ groups. Eligible participants were current college or university students in China who could complete the questionnaire online and provide electronic informed consent before entering the survey. Because recruitment relied on open dissemination through student networks, a formal denominator and conventional response rate could not be calculated. Reporting of the web-based survey was informed by CHERRIES where applicable, and the manuscript follows the STROBE recommendations for cross-sectional studies.\u003c/p\u003e \u003cp\u003eThe parent survey contained 2,413 valid questionnaires after data cleaning. For the present paper, we restricted the analytic sample to 1,681 respondents who reported previous vaginal or anal intercourse. Recruitment in the parent survey was concentrated in Zhejiang (43.1%), Jiangsu (28.8%), and Shanghai (21.8%), with smaller contributions from Beijing (0.7%) and other provinces/regions (5.5%). The survey should therefore be interpreted as multicenter but regionally clustered in Eastern China rather than nationally representative.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData cleaning\u003c/h3\u003e\n\u003cp\u003eQuestionnaires were screened before analysis for logic conflicts, contradictions, and implausible completion patterns. The cleaned export contained no standard missing values, but it did include a small number of implausible derived values. Five implausible birth dates and 14 invalid age-at-sexual-debut entries (11 skipped responses and 3 impossible numeric values) were corrected using prespecified deterministic median imputation within academic-stage or sex strata. No additional missing data remained in the final analytic dataset.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eBehavioral and adverse outcome items were adapted from measures used in the National College Student Survey on Sexual and Reproductive Health in China [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. We intentionally separated direct sexual risk behaviors from downstream adverse outcomes. The primary outcome was clustered direct sexual risk behavior, defined as reporting at least 2 of 4 direct behaviors: no contraception at first sex, casual sex, multiple concurrent partners, and drug-facilitated sex. A stricter sensitivity outcome used a threshold of at least 3 of these 4 behaviors. Secondary outcomes included each direct behavior separately, any adverse sex-related outcome (at least 1 of unintended pregnancy/impregnating someone, abortion, or self-reported STI diagnosis), and each adverse outcome separately.\u003c/p\u003e \u003cp\u003eeHealth literacy was assessed with the 8-item eHEALS using 5 response options from strongly disagree to strongly agree (range 8\u0026ndash;40), with higher scores indicating higher perceived eHealth literacy [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. HIV/STI knowledge was measured using 29 true/false items adapted from the ARCSHS National Survey of Australian Secondary Students and Sexual Health and the RUSSL sexual health literacy study. Correct answers were scored using the prespecified investigator-provided answer key, producing a total score from 0 to 29. Because the items span multiple domains, including HIV transmission, STI symptoms, hepatitis, HPV, vaccination, and PrEP/PEP, we treated the knowledge score as a formative knowledge index rather than as a unidimensional reflective scale.\u003c/p\u003e \u003cp\u003eChildhood/adolescent sexual victimization was categorized into 3 mutually exclusive groups: none, harassment only, and contact/coercive victimization. Harassment-only exposure included online or real-world verbal sexual harassment without physical contact. The contact/coercive category included forced exposure of breasts or genitals, nonconsensual touching of breasts or genitals, nonconsensual oral sex, and nonconsensual vaginal or anal intercourse. Sex education was measured as a binary self-report item asking whether the participant had ever received sex education. Covariates were sex, current age, institution type, relationship status, parental education, monthly expenditure, family structure, smoking, alcohol use, physical activity, and age at sexual debut.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe summarized categorical variables as counts and percentages and continuous variables as means and standard deviations. Internal consistency was evaluated with Cronbach's alpha for eHEALS and Kuder-Richardson Formula 20 (KR-20) for the 29-item knowledge index. The correlation between eHEALS and knowledge was assessed with Pearson correlation. Because the primary outcome was common, we used modified Poisson regression with robust variance to estimate adjusted prevalence ratios (aPRs), which are more interpretable than odds ratios when binary outcomes are not rare [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The primary multivariable model retained eHEALS a priori as a prespecified exposure of substantive interest. Sensitivity analyses included a model excluding eHEALS, a stricter primary outcome threshold (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3 of 4 behaviors), an exploratory eHEALS quartile model, an exploratory domain-specific knowledge model, and a sex-by-victimization interaction model. Analyses were performed in Python 3.11, and two-sided P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Because the data were cross-sectional, all multivariable estimates were interpreted as associational rather than causal.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristics and measurement properties\u003c/h2\u003e \u003cp\u003eThe analytic sample comprised 1,681 sexually experienced students. Men accounted for 60.8% of participants. The mean approximate age was 21.5 (SD 2.3) years, and the mean age at sexual debut was 19.0 (SD 1.7) years after correction of a small number of invalid entries. The sample included 68.8% with no reported childhood/adolescent sexual victimization, 20.0% with harassment-only exposure, and 11.2% with contact/coercive victimization. eHEALS averaged 28.2 (SD 7.1), and the knowledge index averaged 16.3 (SD 3.7). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the sample characteristics by primary-outcome status.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics by clustered direct sexual risk behavior status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2 behaviors, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;=2 behaviors, n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at sexual debut, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeHEALS score, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge score, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1022 (60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e618 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404 (66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e659 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202 (33.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInstitution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1069 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e714 (66.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e355 (58.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e985 university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e211 university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVocational college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (22.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelationship\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever dated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently in relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e939 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e582 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e357 (58.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreviously dated, now separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e707 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e468 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239 (39.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParentedu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (17.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school/secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e847 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e547 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300 (49.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e367 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143 (23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpending\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1500 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1500\u0026ndash;1999 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e571 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e373 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198 (32.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2999 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e745 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282 (46.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u0026ndash;3999 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (12.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=4000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntact family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1419 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e935 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e484 (79.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther family structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (20.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e850 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600 (55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250 (41.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e831 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e475 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356 (58.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRare/none\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1319 (78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e882 (82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e437 (72.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequent alcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (27.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSexedu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived sex education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e957 (56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e614 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e343 (56.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo sex education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e724 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e263 (43.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVictim\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1156 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e778 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e378 (62.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarassment only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (20.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContact/coercive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (17.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003ePercentages in the outcome columns are column percentages.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eeHEALS showed excellent internal consistency (Cronbach's alpha\u0026thinsp;=\u0026thinsp;0.93). In contrast, the 29-item knowledge index had limited internal consistency (KR-20\u0026thinsp;=\u0026thinsp;0.51), consistent with the heterogeneity of its item content. eHEALS and the knowledge index were only weakly correlated (r\u0026thinsp;=\u0026thinsp;0.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Detailed item-level performance is provided in the Supplementary Materials.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrevalence of direct sexual risk behaviors and adverse outcomes\u003c/h3\u003e\n\u003cp\u003eDirect sexual risk behaviors were common. No contraception at first sex was reported by 65.0% of participants, casual sex by 45.6%, drug-facilitated sex by 18.4%, and multiple concurrent partners by 14.9%. Overall, 36.0% reported at least 2 of the 4 direct behavior items and 9.1% reported at least 3. Adverse sex-related outcomes were less frequent but still notable: 7.3% reported unintended pregnancy or impregnating someone, 10.7% reported abortion or causing a partner abortion, 2.7% reported a self-reported STI diagnosis, and 16.5% reported at least one adverse outcome. These prevalences are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of direct sexual risk behaviors and adverse sex-related outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome / behavior\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo contraception at first sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCasual sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple concurrent partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug-facilitated sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClustered direct sexual risk behaviors (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;2 of 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClustered direct sexual risk behaviors (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3 of 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny adverse sex-related outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnintended pregnancy / impregnating someone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbortion / causing a partner abortion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported STI diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eClustered direct sexual risk behavior was defined as reporting at least 2 of 4 direct behaviors.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePsychometric summary of eHEALS and the 29-item knowledge index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeHEALS total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29-item HIV/STI knowledge index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKR-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearson correlation between eHEALS and knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er (P value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eThe knowledge measure was treated as a formative index because the item pool covered heterogeneous content domains.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePrimary multivariable model\u003c/h3\u003e\n\u003cp\u003eIn the fully adjusted modified Poisson model for clustered direct sexual risk behaviors, contact/coercive victimization was the strongest and most consistent correlate (aPR 1.56, 95% CI 1.31\u0026ndash;1.85). Male sex (aPR 1.26, 95% CI 1.08\u0026ndash;1.46), current age (aPR 1.04 per year, 95% CI 1.00-1.07), current smoking (aPR 1.22, 95% CI 1.05\u0026ndash;1.41), and higher knowledge score (aPR 1.15 per 5 points, 95% CI 1.05\u0026ndash;1.25) were associated with a higher prevalence of clustered direct risk behaviors. Intact family structure (aPR 0.78, 95% CI 0.67\u0026ndash;0.91) and older age at sexual debut (aPR 0.93 per year, 95% CI 0.89\u0026ndash;0.98) were associated with a lower prevalence. Harassment-only exposure was borderline (aPR 1.17, 95% CI 1.00-1.38), and eHEALS was not independently associated with the primary outcome in the continuous model (aPR 0.96 per 5 points, 95% CI 0.92\u0026ndash;1.01). Main model estimates are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModified Poisson regression for clustered direct sexual risk behaviors (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;2 of 4 behaviors)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude PR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted PR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarassment only (vs none)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (0.94\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (1.00-1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContact/coercive victimization (vs none)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72 (1.48\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56 (1.31\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeHEALS (per 5-point increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.91\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.92\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge index (per 5-point increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.03\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (1.05\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived sex education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.87\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.88\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29 (1.12\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.08\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent age (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (1.00-1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntact family structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.63\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.67\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46 (1.28\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.05\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequent alcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41 (1.23\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.99\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at sexual debut (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.89\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.89\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eThe adjusted model additionally included institution type, relationship status, parental education, monthly expenditure, and physical activity.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSecondary and sensitivity analyses\u003c/h2\u003e \u003cp\u003eSecondary analyses revealed a more differentiated pattern. Higher eHEALS was associated with lower prevalence of no contraception at first sex (aPR 0.97, 95% CI 0.95-1.00) and lower prevalence of any adverse sex-related outcome (aPR 0.90, 95% CI 0.84\u0026ndash;0.97), but it was not clearly associated with clustered direct sexual risk behaviors as a whole. Contact/coercive victimization was strongly associated with multiple concurrent partners (aPR 2.51, 95% CI 1.88\u0026ndash;3.36), abortion (aPR 1.54, 95% CI 1.10\u0026ndash;2.15), self-reported STI diagnosis (aPR 3.11, 95% CI 1.63\u0026ndash;5.96), and any adverse sex-related outcome (aPR 1.69, 95% CI 1.31\u0026ndash;2.17). Selected secondary models are provided in the Supplementary Materials.\u003c/p\u003e \u003cp\u003eSensitivity analyses did not materially change the main pattern of findings. Excluding eHEALS from the primary model left the estimates for victimization, knowledge, sex, smoking, and age at sexual debut largely unchanged. In the stricter\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3-behavior model, contact/coercive victimization, frequent alcohol use, male sex, and earlier sexual debut remained important correlates. The exploratory eHEALS quartile model suggested a non-monotonic pattern, with lower adjusted prevalence in the second and third quartiles relative to the lowest quartile but no clear gradient in the highest quartile. In an exploratory domain-specific knowledge model, only the HIV knowledge domain was positively associated with the primary outcome, whereas STI/HPV and hepatitis subdomains were not. A sex-by-victimization interaction suggested that the association between contact/coercive victimization and clustered direct risk behaviors was stronger among women than among men.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study adds to the literature by focusing on heterogeneity within sexually experienced college students rather than by contrasting sexually active and sexually inactive groups. Three findings deserve emphasis. First, direct sexual risk behavior clustering was common, with more than one third of respondents reporting at least 2 direct behavior items. Second, childhood/adolescent contact or coercive victimization was the strongest and most consistent correlate of both clustered direct risk behaviors and downstream adverse outcomes. Third, the expected protective roles of eHEALS and factual HIV/STI knowledge were not supported in a simple linear way.\u003c/p\u003e \u003cp\u003eThe victimization findings are especially important for campus health policy and practice. Distinguishing harassment-only exposure from contact/coercive victimization improved interpretability. After adjustment, harassment-only exposure showed only limited association with the primary outcome, whereas contact/coercive victimization remained robustly associated with clustered risk behavior and multiple adverse outcomes. This pattern is consistent with the potential value of trauma-informed campus prevention, screening, and referral pathways. Our exploratory interaction analysis further suggested that the victimization-risk association may be stronger among women, even though the overall prevalence of clustered direct risk behavior was higher among men. That interpretation is consistent with gender-sensitive models in which men may have higher baseline behavioral exposure, while the downstream consequences of coercive trauma may be especially pronounced among women.\u003c/p\u003e \u003cp\u003eThe eHEALS findings were more nuanced than a simple protective-hypothesis model. eHEALS was not independently associated with the behavior-only primary outcome in the continuous model, but it was associated with lower prevalence of no contraception at first sex and lower prevalence of any adverse sex-related outcome. One plausible interpretation is that self-perceived digital health literacy may help students navigate consequences, information seeking, and help-seeking once risk emerges, even if it does not fully prevent clustering of direct behaviors. The weak correlation between eHEALS and the knowledge index also suggests that they capture related but distinct constructs: perceived digital self-efficacy versus retained factual sexual health content. This finding adds to ongoing discussion about how digital health literacy should be operationalized in sexual-health research among young adults in culturally specific settings [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe positive association between the knowledge index and the primary outcome should not be interpreted causally as evidence that knowledge increases risk. The effect size was modest, the cross-sectional design leaves reverse temporality plausible, and the exploratory domain model suggested that this pattern was driven mainly by the HIV knowledge subdomain rather than by STI/HPV or hepatitis content. Students with greater exposure to sexual risk may subsequently encounter HIV-related education, peer discussion, testing services, or online content and thus score higher on some knowledge items. Importantly, even after we rescored the 29 items using the prespecified investigator-provided answer key, KR-20 remained low. This indicates that the measurement problem is not simply one of miscoding; instead, the item pool appears to function as a heterogeneous knowledge index rather than as a coherent single latent scale.\u003c/p\u003e \u003cp\u003eSex education should also be interpreted cautiously in this study. Our measure captured only whether participants had ever received sex education. It did not distinguish timing, content, skill components, delivery format, or whether the exposure occurred before or after sexual debut. Accordingly, the absence of a protective association in the primary model, and even the positive associations seen in some secondary models, should not be read as evidence that sex education is ineffective. Rather, they show that a simple yes/no item is a poor proxy for the quality or timing of sexuality education.\u003c/p\u003e \u003cp\u003eThis study has several strengths. It focused on sexually experienced students, reported design and ethics information transparently, separated direct sexual behaviors from downstream adverse outcomes, used a documented answer key for the knowledge items, and estimated prevalence ratios rather than odds ratios for a common binary outcome. The study also included multiple sensitivity and exploratory analyses, including stricter outcome definitions, models excluding eHEALS, domain-specific knowledge models, and sex-by-victimization interaction analyses.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. First, the cross-sectional design precludes causal inference, and reverse temporality is plausible for associations involving eHealth literacy, knowledge, sex education, and adverse outcomes. Second, all measures were self-reported and may be affected by recall error and social desirability bias, particularly for sensitive sexual behaviors, abortion history, STI diagnosis, and victimization. Third, recruitment occurred through open dissemination in university-related WeChat and QQ groups, so a denominator and conventional response rate could not be calculated; sampling weights were unavailable, and selection bias is possible if students who were more connected to these networks or more willing to disclose sexual experiences were more likely to participate. Fourth, the sample was multicenter but regionally clustered in Eastern China and is not nationally representative. The sexually experienced analytic export did not retain institution identifiers, so the regional distribution is described using the parent survey frame. Fifth, several outcomes were lifetime-type cumulative measures and may therefore correlate with current age. Sixth, the knowledge index showed limited internal consistency and was treated as a heterogeneous formative index rather than a single latent scale, while the sex education measure was coarse and did not capture timing, dose, content, or quality. Finally, although only a small number of implausible age-related values required deterministic correction, and no additional missing data remained after cleaning, residual measurement error and unmeasured confounding cannot be excluded.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAmong sexually experienced college students drawn from a multicenter online survey in Eastern China, clustered direct sexual risk behaviors were common and were most strongly associated with childhood/adolescent contact or coercive sexual victimization, earlier sexual debut, current smoking, and male sex. Higher eHealth literacy was not independently associated with the behavior-only primary outcome, but it was associated with lower prevalence of adverse sex-related outcomes. These cross-sectional findings suggest that universities may benefit from moving beyond information provision alone and considering trauma-informed, skills-based, and gender-sensitive sexual health support.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eaPR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadjusted prevalence ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCHERRIES\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChecklist for Reporting Results of Internet E-Surveys\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eeHEALS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeHealth Literacy Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHIV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman immunodeficiency virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHPV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman papillomavirus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKR-20\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKuder-Richardson Formula 20\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePrEP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epre-exposure prophylaxis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePEP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epost-exposure prophylaxis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esexually transmitted infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTROBE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003e The study protocol was approved by the Ethics Committee of Jiangsu College of Nursing (approval No. JSCN-ME-2025070719). All participants provided electronic informed consent before entering the online questionnaire.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQiufeng Liu conceived the study, oversaw data collection, and supervised the project. Qianxu Yang conducted the statistical analysis, prepared the figures and tables, and drafted the manuscript. Yee Yin Hoo contributed to methodology, interpretation, and critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the participating students and the student recruiters who assisted with questionnaire dissemination through university WeChat and QQ groups. The authors are also grateful to Taizhou University, Zhejiang University, Ningbo University, Wuxi University, Shanghai University of Electric Power, East China University of Science and Technology, Nanjing University, Shanghai Ocean University, and Wenzhou University for recruitment support and study dissemination.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe de-identified analytic dataset and statistical code that support the findings of this study are not publicly available because the survey addressed sensitive sexual health information and the ethics approval did not include unrestricted public release. They are available from the corresponding author on reasonable request, subject to approval by the Ethics Committee of Jiangsu College of Nursing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Sexually transmitted infections (STIs). Geneva: WHO; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld. Health Organization. Condoms. Geneva: WHO; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorman CD, Skinner HA. eHEALS: The eHealth Literacy Scale. J Med Internet Res. 2006;8(4):e27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/jmir.8.4.e27\u003c/span\u003e\u003cspan address=\"10.2196/jmir.8.4.e27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong C, Zheng L, Liu R, Duan Z. 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A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kwh090\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwh090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"reproductive-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reph","sideBox":"Learn more about [Reproductive Health](http://reproductive-health-journal.biomedcentral.com)","snPcode":"12978","submissionUrl":"https://submission.nature.com/new-submission/12978/3","title":"Reproductive Health","twitterHandle":"@Reprod_Health","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"college students, sexual risk behavior, eHealth literacy, HIV knowledge, sexual victimization, trauma-informed care, China","lastPublishedDoi":"10.21203/rs.3.rs-9146107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9146107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRisky sexual behavior remains an important public health concern among university students, but the roles of digital health literacy and sexual health knowledge are still unclear in Chinese settings. We examined clustered direct sexual risk behaviors and adverse sex-related outcomes among sexually experienced college students in Eastern China, with particular attention to eHealth literacy, HIV/STI knowledge, sex education, and childhood/adolescent sexual victimization.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed 1,681 sexually experienced participants drawn from a multicenter online survey conducted in October 2025 through Wenjuanxing. Student recruiters disseminated the survey through university-related WeChat and QQ groups. Because recruitment relied on open dissemination through student networks, a conventional response rate could not be calculated. The primary outcome was clustered direct sexual risk behavior, defined as reporting at least 2 of 4 direct behaviors: no contraception at first sex, casual sex, multiple concurrent partners, and drug-facilitated sex. A secondary composite captured any adverse sex-related outcome (unintended pregnancy/impregnation, abortion, or self-reported STI diagnosis). eHealth literacy was measured with the 8-item eHEALS, and HIV/STI knowledge was scored using a prespecified 29-item answer key. Modified Poisson regression with robust variance estimated adjusted prevalence ratios (aPRs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eParticipants were predominantly male (60.8%) and had a mean age of 21.5 (SD 2.3) years. The mean eHEALS score was 28.2 (SD 7.1); the mean knowledge score was 16.3 (SD 3.7). No contraception at first sex (65.0%) and casual sex (45.6%) were common. Overall, 36.0% reported clustered direct sexual risk behaviors and 16.5% reported at least one adverse sex-related outcome. eHEALS showed excellent internal consistency (Cronbach's alpha\u0026thinsp;=\u0026thinsp;0.93), whereas the 29-item knowledge index had limited internal consistency (KR-20\u0026thinsp;=\u0026thinsp;0.51). In the adjusted primary model, contact/coercive victimization was strongly associated with clustered direct sexual risk behaviors (aPR 1.56, 95% CI 1.31\u0026ndash;1.85), as were male sex (aPR 1.26, 95% CI 1.08\u0026ndash;1.46), current smoking (aPR 1.22, 95% CI 1.05\u0026ndash;1.41), older current age (aPR 1.04 per year, 95% CI 1.00-1.07), and higher knowledge score (aPR 1.15 per 5 points, 95% CI 1.05\u0026ndash;1.25). Intact family structure (aPR 0.78, 95% CI 0.67\u0026ndash;0.91) and older age at sexual debut (aPR 0.93 per year, 95% CI 0.89\u0026ndash;0.98) were protective. eHEALS was not independently associated with the primary outcome (aPR 0.96 per 5 points, 95% CI 0.92\u0026ndash;1.01). In contrast, higher eHEALS was associated with a lower prevalence of any adverse sex-related outcome (aPR 0.90, 95% CI 0.84\u0026ndash;0.97).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAmong sexually experienced college students in Eastern China, clustered direct sexual risk behaviors were common and were most consistently associated with contact/coercive victimization, earlier sexual debut, smoking, and male sex. Higher eHealth literacy was not independently associated with the behavior-only primary outcome, but it was associated with lower prevalence of adverse sex-related outcomes. These cross-sectional findings suggest that campus sexual health programs may benefit from trauma-informed, skills-based, and gender-sensitive support rather than relying on information provision alone.\u003c/p\u003e","manuscriptTitle":"Clustered direct sexual risk behaviors and adverse sex-related outcomes among sexually experienced college students in Eastern China: a multicenter cross-sectional study of eHealth literacy, HIV/STI knowledge, and childhood/adolescent sexual victimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 05:48:32","doi":"10.21203/rs.3.rs-9146107/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-14T12:54:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T06:20:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Reproductive Health","date":"2026-04-07T08:52:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"reproductive-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reph","sideBox":"Learn more about [Reproductive Health](http://reproductive-health-journal.biomedcentral.com)","snPcode":"12978","submissionUrl":"https://submission.nature.com/new-submission/12978/3","title":"Reproductive Health","twitterHandle":"@Reprod_Health","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a778d092-6bd2-43e5-844d-1bb6af8ca506","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T05:48:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 05:48:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9146107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9146107","identity":"rs-9146107","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}