Sexual conversation networks and young adults' sexual health in a Southeast-European context.
Social networks (Public opinion)
Teenagers (Public opinion)
Youth (Public opinion)
HIV (Viruses) (Public opinion)
Condoms (Public opinion)
|Publication:||Name: The Canadian Journal of Human Sexuality Publisher: SIECCAN, The Sex Information and Education Council of Canada Audience: Academic Format: Magazine/Journal Subject: Psychology and mental health Copyright: COPYRIGHT 2011 SIECCAN, The Sex Information and Education Council of Canada ISSN: 1188-4517|
|Issue:||Date: Winter, 2011 Source Volume: 20 Source Issue: 4|
|Topic:||Event Code: 290 Public affairs|
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|Organization:||Company Name: Oxford University Press (Oxford, England)|
|Geographic:||Geographic Scope: Croatia; United Kingdom; New York Geographic Code: 4EXCR Croatia; 1U2NY New York|
Abstract: Social network approach has conceptually and empirically
improved research on HIV/ AIDS by counterbalancing the individualized
concepts of sexual decision-making. In the current study, associations
between the structural characteristics of ego-centered networks and
HIV-related knowledge, attitudes, and behaviours were examined in a
population-based sample (n = 1005) of young Croatian adults aged 18-25
years. The findings suggested that the associations between the selected
HIV-relevant outcomes and the structural properties of social networks
were weak and markedly gender-specific. Among women, network-based
religiosity predicted HIV-related knowledge, while network density was
related to attitudes toward condoms. Age homophily and network history
were correlated with condom use at last intercourse among men. Future
studies may improve the understanding of the associations between social
networks and HIV-relevant beliefs and behaviours by exploring the
possibility that network effects are not invariant over time.
Network analysis has been increasingly successful in improving our knowledge of the social dimensions of health (Luke & Harris, 2007; Smith & Christakis, 2008). We now know, for example, that sexual health-related behaviours, knowledge, and attitudes in particular are strongly shaped by the immediate social environment through peer influence and normative pressures. To date, a majority of population-based studies of social networks and sexuality have been carried out in North America (e.g., Bearman, Moody, & Stovel, 2004; Laumann, Gagnon, Michael, & Michaels, 1994) although the human-immunodeficiency virus (HIV) epidemic has motivated a number of innovative network studies in sub-Saharan Africa (e.g., Helleringer & Kohler, 2007; Kohler, 2001; Morris, 2004; Valente, Watkins, Jato, van der Straten, & Tsitsol, 1997). In contrast, similar studies of the social dimensions of sexuality and sexually transmitted infections (STIs) have yet to appear in other parts of the world, including Southeastern Europe.
The characteristics of social networks, including tie strength (Granovetter, 1983) and homophily (i.e., the tendency to create ties with similar people; McPherson, Smith-Lovin, & Cook, 2001), are crucial for explaining how individuals behave and understand the world around them. These and other dimensions of social connections have been associated with a range of sexual practices of young people, including contraceptive use (Choi & Gregorich, 2009; Tyler, 2008), timing of intercourse (Sieving, Eisenberg, Pettingell, & Skay, 2006), sexual debut (Adamczyk & Felson, 2006), and STI-related risks (Fichtenberg, Muth, Brown, Padian, Glass, & Ellen, 2009). There is also qualitative and quantitative evidence that, especially among women, informal conversations about topic such as family planning, family size, and HIV/AIDS are associated with fertility-related attitudes (Bernardi, 2003; Montgomery and Chung 1998; Smith & Watkins, 2005). Men engage in similar conversations when, for example, they seek sexual health information from each other or ask for advice about STI treatment (Watkins, 2004).
Personal networks and their properties influence individuals through a variety of social mechanisms (Luke & Harris, 2007) although two mechanisms, social learning and social influence, appear to be central (Christakis & Fowler, 2009; Valente, 2010). Individuals learn about health-related topics from the people around them and that is particularly so for topics dealing with sexual and reproductive health (Kohler, 2001). Social connections also influence people's beliefs and attitudes through social norms that exist in their environment. Such normative influences on sex-related issues are pronounced among young people who are more likely than adults to imitate what their peers are doing and thinking. In addition, the personal reputations of adolescents and young adults seem to be more dependent on the referent group opinion than is the case with adults, which further facilitates these normative pressures.
The present study
The aim of the present study was to examine the associations between Croatian young adults' personal networks and HIV-relevant attitudes, knowledge, and behaviours. The guiding assumption behind the study, based on the vast literature on youth sexuality, was that the basic structural characteristics of social networks, e.g., size, density, or homophily, would be related to a number of sexual and reproductive health outcomes. Given that sexual socialization of women and men still diverges, particularly in Southern Europe (cf. Traeen, Stulhofer, & Landripet, 2011), we also expected that the observed pattern of associations between network characteristics and outcomes would be gender-specific.
As is the case with a number of its neighbouring countries, Croatia has witnessed significant political, socio-economic, and cultural changes in the last 20 years triggered by the process of post-communist transition (Stulhofer & Sandfort, 2005). The unemployment level remains high and the GDP per capita is substantially lower than in Western European countries. According to the Croatian National Bank, GDP per capita in 2011 was 18,300 USD, and the unemployment rate was 17.9%. In addition, the effects of the current global recession have had severe consequences on the public sector prompting cuts in social services, including investment in HIV prevention programs. In the sphere of sexuality, this social change has been marked by the competing influences of rising sexual permissiveness on the one hand and politically motivated efforts at retraditionalization on the other. While popular media and increasing Internet usage have stimulated the former, the Catholic Church has played an important role in the latter (Bijelic, 2008). In such a tumultuous social context, it can be argued that social networks may reflect clashing normative pressures by mediating their influence on what young people know and believe about sexrelated topics. For example, the stigma attached to HIV and its association with socially marginalized groups such as homosexual men, could have distorted the type of information that young people have available about the infection. In addition, the moral discourse surrounding contraception as reflecting a "promiscuous lifestyle" may have discouraged some young men and women from developing positive attitudes towards condom use.
Sexual knowledge, attitudes and behaviours
In order to gain a better understanding of HIV-related sexual behaviours, knowledge and attitudes among young people, we examined here the associations between risky sexual behaviours, HIV knowledge and attitudes toward condom use and a number of characteristics of ego-centered social networks (ESN) related to communication about sexuality in a population-based sample of Croatian young adults aged 18-25. Considering that sex-related norms are often gender-specific (Ricardo, Barker, Pulerwitz, & Rocha, 2006), particularly in more traditional settings, we explored whether the associations that we found would reflect this traditional difference between women and men. Our analysis emerges from the theoretical notion that network-based approaches provide a more socially embedded (and therefore, more realistic) understanding of HIV-relevant attitudes and knowledge than do individualistic, socially atomized approaches (Boyce, Huang Soo, Jenkins, Mohamed, Overs et al., 2007; Obermayer, 2004; Rhodes, 1997; Rhodes, Singer, Bourgois, Friedman, & Strathdee, 2005).
Participants and data collection
In February and March 2010, a national probability study of sexuality-related knowledge, attitudes, and behaviors was carried out among 1,005 Croatian young adults aged 18-25. The study procedures were approved by the Ethical Review Board of the Faculty of Humanities and Social Sciences at the University of Zagreb and informed consent was obtained from all participants. The sample was stratified according to region (n = 6), county (n = 21), settlement size ([less than or equal to] 2,000; 2,001 - 10,000; 10,001 - 100,000; > 100,000), and gender. The survey was completed by 495 women and 510 men who were surveyed in their homes. The overall participation rate was 32%. An analysis of reasons given for refusal showed that 24% of those who refused to participate declined to discuss the subject of the study (HIV-related issues), while an additional 4% mentioned parents' opposition to their participation. Around three quarters of participants (74.1% of women and 71.6% of men) described their family socioeconomic status as average, while about one fifth of women (22.6%) and one fourth of men (24.3%) assessed it as better than average. A majority of participants (54.8%) reported spending most of their life in settlements with less than 10,000 inhabitants (54.8%). Religious background was almost invariably Catholic (most participants stated that they were raised in a religious spirit in their families), but only 28.3% of women and 25.7% of men who reported this upbringing strictly followed religious norms. At the time of the survey, 9.3% of women and 4.9% of men were married, while 51.9% of women and 43.3% of men reported being in a relationship. The mean age at first sexual intercourse was 16.9 years (SD = 1.86) for men and 17.4 years (SD = 1.59) for women. Median age was 17 for both genders. A majority of participants were currently sexually active (85.7%). On average, men reported 7.3 lifetime sexual partners (SD = 9.97) and women 3.7 (SD = 4.35).
The first part of the questionnaire was administered by a trained interviewer. Information on sociodemographic characteristics, HIV knowledge, gender roles, and ESN was collected. The second part of the questionnaire, which focused on sexual behaviours, was self-administered to maximize confidentiality.
The network questions were fashioned according to the General Social Survey measures designed by Burt (1984). We used the following name generator for eliciting personal networks: "Many people occasionally talk about sexuality with their friends and other close persons; please list the persons with whom you talk about your own sexual life most often--excluding your parents." Participants were allowed to name up to five individuals (alters) and were then asked about the age, sex, education, and religiosity of the alters cited, and also how long the participant (ego) had known each of the alters. In addition, relationships among the alters were assessed. Only 59 participants (5.9%) reported no alters. The questionnaire was piloted for comprehensiveness and completion time among 103 students from two metropolitan secondary schools and 132 university students.
Knowledge of HIV was assessed with seven questions about the modes of HIV transmission (including widespread misconceptions) and the modes of protection from HIV infection, which were recommended as United Nations General Assembly Special Session indicators (UNAIDS, 2007): (1) Can the risk of HIV transmission be reduced by having sex with only one uninfected partner who has no other partners? (2) Can a person reduce the risk of getting HIV by using a condom every time they have sex? (3) Can a healthy-looking person have HIV? (we used the variety "Can one get HIV by having sex with a healthy-looking person?") (4) Can a person get HIV by sharing food with someone who is infected? (5) Can a person get HIV from mosquito bites'? (6) Can a person get HIV by using a public toilet? and (7) Can a person get HIV by using a glass after someone who is infected? Correct answers were coded 1 and others 0 giving a score range from 0 to 7 with higher results indicating more knowledge.
Attitudes toward condoms
Participants' perspectives on condom use were addressed on a four item scale that included observations such as "Those who suggest condom use frequently change sexual partners" and "Individuals who insist on condom use mistrust their partners". Answers for each item were recorded on a 5-point Likert-type scale with higher results reflecting higher acceptance of condom use. The items were summed into a composite scale with satisfactory reliability ([alpha] =.80).
Indicators of sexual risk taking
Two items were used to assess sexual risk-taking: (a) condom use at most recent sexual intercourse and (b) the number of sexual partners in the last 12 months (sexual partner was defined as someone with whom the participant had sexual intercourse). The second indicator was dichotomized into 0 = one or no sexual partners and 1 = two or more sexual partners in the past year.
Characteristics of ego-centered social networks
Six characteristics of ego-centered social networks were assessed: network size (0 - 1,2 - 3, [greater than or equal to] 4); network density; network-based religiosity; age; gender homophily; and network history. Network density assessed the level of interpersonal connectedness (number of dyadic ties) within ego-centered networks. The indicator was dichotomized into 0 = less than the maximum number of ties between pairs of alters, and 1 = maximum number of ties. Network-based religiosity refers to the number of alters that the ego classified as attending religious services regularly. According to the answers to that question, we compiled three categories: 1 = no religious alters, 2 = some alters are religious and 3 = all alters are religious. Age homophily (1 = on average, alters are younger than ego, 2 = alters are of the same age as ego, and 3 = alters are older than ego) and gender homophily (1 = not all alters of the same gender as ego, and 2 = all alters of the same gender as ego) assessed age and gender composition of one's social network. Only one participant reported a social network with all alters of the opposite gender. Finally, network history, which indicated the average duration of ego's relationships with alters, was recoded into 1 = 1 - 5 years, 2 = 5.1 - 10 years, and 3 = [greater than or equal to] 10.1 years of knowing alters.
Socio-demographic indicators were age, the size of settlement where the participant spent most of his/ her life ([less than or equal to] 2,000 inhabitants; 2,001 - 10,000; 10,001 100,000; [greater than or equal to] 100,000), parents' education, sexual experience, and frequency of church attendance. Parents' education consisted of summed mother's and father's education (1 = primary education or less, 2 = secondary and 3 = tertiary education). Sexual experience was defined as having had sexual intercourse. Frequency of church attendance was measured on a 6-point scale (1 = never to 6 = daily or almost daily).
Gender differences in basic characteristics of ESN, as well as associations between ESN characteristics and risky sexual behaviours, were assessed with chi-square tests. One-way ANOVA was used to test bivariate associations between ESN characteristics one the one hand, and HIV knowledge and attitudes toward condom use on the other. Finally, multivariate OLS and logistic regression analyses were carried out to verify statistically significant bivariate ties by controlling for selected sociodemographic characteristics (age, settlement size, parents' education, frequency of attending religious services, and--in two of the three analyses--sexual experience). All data analyses were carried out using SPSS 16 statistical software. All analyses were conducted separately for women and men to assess the likely effects of gender-specific sexual socialization.
The basic characteristics of personal network are presented by gender in Table 1. Male and female personal networks differed significantly with respect to network religiosity and age homophily. Women reported more religious alters in their social networks than men. In addition, women had proportionately more older alters than did men, while the opposite was found in reporting younger alters.
Bivariate associations between ego-centered network characteristics and HIV-related knowledge, attitudes toward condoms, and HIV risk behaviours are presented in Table 2. Among men, the largest networks were associated with lowest HIV knowledge, but also with most accepting attitudes toward condom use. With regard to HIV-risk behaviours, network age homophily and network history were significantly related to condom use at most recent sexual intercourse. Among the men who used a condom at most recent intercourse, those who reported younger alters were significantly less prevalent than others. Interestingly, the highest proportion of participants who reported condom use was found among men with the shortest personal network history (up to five years).
Among women, HIV knowledge was found to be associated with network-based religiosity. Women who reported only religious alters were significantly less informed about HIV than other female participants. The female participants' attitudes toward condom use were related to three network characteristics. Women who reported the smallest and least dense networks had the least positive attitudes toward condom use. The least positive attitudes toward condoms were also observed among women who discussed their sex life only with religious alters. In the female subsample, risky sexual behaviours were related only to personal network size. Women who reported the smallest network size were significantly under-represented among those who reported that a condom was used at their most recent intercourse. The association with the second indicator of sexual risk taking (partners in the last 12 months) was in the opposite direction. The highest proportion of women with a single sexual partner (or no partners) in the past 12 months was observed among those with the smallest personal networks.
Linear and logistic multivariate regression analyses
As the final step, linear (Table 3) and logistic (Table 4) multivariate regression analyses were carried out with sociodemographic indicators and selected network characteristics as correlates of HIV knowledge and attitudes toward condoms. As shown in Table 3, HIV knowledge among men was significantly predicted by their parents' education ([beta] =. 15, p <.01) and by their own frequency of church attendance ([beta] = -. 15, p < .01). Frequency of church attendance was also found to be negatively associated with male participants' attitudes toward condom use ([beta] = -. 15, p <.01).
In the female subsample, HIV knowledge was significantly related to parents' education ([beta] =. 14, p <.01) and network-based religiosity ([beta] = -. 13, p < .05) (Table 3). Women who reported only religious alters were significantly less knowledgeable about HIV than those with no religious alters. Women's attitudes toward condom use were associated with settlement size ([beta] =. 14, p <.01), personal religiosity ([beta] = -.17, p <.01), and network density ([beta] =.12, p < .05). Female participants living in the largest urban settings and those with the highest network density held more positive attitudes toward condom use than others. Women with a higher frequency of church attendance were less accepting of condom use.
All four linear regression models explained marginal proportions of the dependent variables' variance.
The correlates of sexual risk behaviours are shown in Table 4. Among men, two personal network characteristics were significantly related to condom use at most recent sexual intercourse. Men who reported younger alters were substantially less likely to have used a condom at most recent intercourse (OR =.58, p <.05) than other male participants. In addition, those with the shortest network history were 1.7 times more likely to have used a condom than men with the longest network history (OR = 1.71, p <.05). Reporting multiple sexual partners in the past 12 months was correlated only with age (OR =.88, p <.05). In the male subsample, the logistic regression analysis correctly predicted 59% of cases when the outcome was condom use at most recent sexual intercourse, which is a marginal improvement over chance. Only 54% of correctly classified cases were observed when predicting the number of sexual partners in the past 12 months.
Among women, parents' education increased the odds of condom use at most recent sexual intercourse (OR = 1.25, p <.05), while women with higher church attendance were significantly less likely to report having had two or more partners in the past year (OR =.78, p <.05). For women, the regression model correctly predicted only 57% of cases when condom use at most recent intercourse was the dependent variable. The prediction was somewhat better in the case of the number of sexual partners (76% of correctly classified cases).
Through social influence and social learning, peers have been identified as playing an important role in the context of youth sexuality (Maxwell, 2002). For most young men and women, talking about sex with friends is more common and more comfortable than discussing it either with parents or health-care providers (Lefkowitz, Boone, & Shearer, 2004). In this study, we assessed peer influence on HIV-related knowledge, attitudes, and behaviours by analyzing young adults' ESN. The finding that a majority of young Croatian adults talk about their sex life with a number of friends (the sex-talk networks appear to be relatively large, with 30% of participants reporting four or more confidants) confirmed the feasibility of approaching the question of peer influence on sexuality-related issues by social network analysis.
Significant gender differences in the composition of personal networks emerged only with regard to network religiosity and age homophily. Unlike male participants, female participants reported that they discuss their sex life with alters who are on average older than themselves. In addition, female networks included more religious alters than male networks. Adjusted for participants' sociodemographic characteristics, only a few network characteristics were significantly correlated with the outcomes. Among women, network-based religiosity predicted HIV-related knowledge, while network density was associated with attitudes toward condoms. No significant associations were found between ESN characteristics and risky sexual behaviours. In contrast, among men age homophily and network history were correlated with condom use at last sexual intercourse.
The fact that female networks were more religious then male ones corresponds to the finding that a negative association between network-based religiosity and HIV-related knowledge was found only among women. The relationship, however, was significant only when all alters were religious, not in the case when ESN included both religious and non-religious alters. This corroborates the results of a previous Croatian study, which showed that religiosity is negatively related to how much young women know about sexuality (Stulhofer, Soh, Jelaska, Bacak, & Landripet, 2010). Although it seems possible that discussing one's sex life only with religious peers may limit the scope of topics and information young women can receive, network-based religiosity did not seem to be associated with attitudes toward condom use. This is in contrast to a recent study carried out in the United States, which found that religious young adults discussed abstinence more than their peers and that discussing abstinence was associated with having more negative attitudes toward condoms (Lefkowitz et al., 2004). It is plausible that the interactions between social networks, religiosity, and sexuality among young female adults are culture-specific--moderated by religious affiliations and/or mediated by the prevalence of religious fundamentalism.
Network density was positively associated with attitudes toward condoms among female participants, similar to some other studies that have examined related aspects of female contraceptive use (e.g., Kohler, Berman, & Watkins, 2001). It is important to note here that in this study density was a binary variable where dense networks were defined as those in which all alters were mutually connected. Such networks are likely to exhibit stronger normative pressure on their members--either due to homophilic selection or emotionally enhanced members' influence (see Billy & Udry, 1985). As condom use is a normatively loaded subject, the finding that network density was related to attitudes toward condom use among women is not surprising. The direction of this relationship is, however, less self-evident. The predominantly positive attitude toward condom use observed in this study suggests that more dense personal networks are more efficient in solidifying this viewpoint among young adults than less dense networks.
Network characteristics were significantly related to sexual behaviour only among men. Similar to the finding of Ford, Sohn, and Lepkowski (2001) that having older sexual partners decreases the likelihood of condom use, we found that men in our study who reported alters younger, on average, than themselves were less likely to report condom use at most recent sexual intercourse. This age discrepancy may be suggestive of a unidirectional path of social influence on sexual behaviour. Younger network members may themselves be more likely not to use condoms due to less experience and a lower likelihood that they have already experienced an adverse consequence of unprotected sex. Through social influence, this may impact ego's practices. If some alters are also ego's sexual partners, an alternative consideration would be that younger partners may have less power to negotiate condom use.
Men who knew their alters for a shorter period of time were more likely to report using a condom at most recent sexual intercourse than those who reported having networks which were 10 or more years-old. Although more information is required to explain this intriguing finding, it could be that more recently formed networks are less homogenous and, thus, allow for more diverse information and experiences to be shared than more restrictive (more homogenous) long-term networks. Alternatively, it could be that the association reflects the effect of education, as more educated participants are often more socially "mobile" (and more likely to use condoms), i.e. more likely to change social circles throughout their educational career.
Overall, the associations between young adults' HIV-related knowledge, attitudes and behaviors on the one hand and the structural properties of their social networks on the other were few and weak. (All multivariate regression models were characterized by a marginal proportion of explained variance.) This is a departure from studies which have found relatively strong independent effects of social network structure on sexual risk taking (Avogo & Agadjanian, 2008; Choi & Gregorich, 2009; Valente, 2010). A possible reason may be that the name generator used in this study was not entirely adequate or that it was too general (we discuss this below). Another possibility to be considered is a timing effect. The importance of personal networks may not be invariant over time. It seems plausible that the influence of alters is more pronounced at some critical stages or moments, such as the onset of partnered sexual activity (or an emotional crisis caused, for example, by a traumatic break up or being diagnosed with an STI), and becomes less important in more steady periods. Although our data do not allow us to test this hypothesis, a timing effect, if it exists, would almost certainly result in weak and inconsistent associations between network characteristics and the outcomes analyzed in this study.
Several study limitations should be briefly discussed. The low response rate may have affected the generalizability of the findings. It likely introduced some selection bias, favouring more sexually open and liberal individuals. Considering that one in four non-participants stated the study subject as their reason for refusing to participate, it may be that individuals who were more likely to discuss their sex life with their friends and acquaintances were overrepresented in our sample. Whether this may have reduced the variability of personal network characteristics or otherwise affected the associations between the characteristics of ESN and HIV-related issues remains unknown, but it cautions against an uncritical extrapolation of the findings to the respective population. It also needs to be emphasized that the findings are not generalizable outside of the South-Eastern European context due to distinct social and cultural influences on youth sexuality in the region.
The name generator used in this study focused on the individual's sex life rather than on discussions regarding sexuality in general or HIV-related issues. Such a strategy likely underestimated the role of social networks in the peer dissemination of HIV knowledge and the social construction of HIV-related attitudes. As HIV risks remain low in Croatia, especially for members of the general population, it is likely that focusing on more personal aspects of sexuality has overlooked some of the peer exchange on HIV-related topics and issues. It is, however, reasonable to expect that those alters with whom ego feels comfortable enough to talk about his/her sex life would also qualify for discussions about HIV risks. In addition, some of the reasons for the weak association between social networks and the HIV-related outcomes examined in this study may lie in a substantial role-topic dependency in conversational networks (Bearman & Parigi, 2004). Future studies should consider collecting more information about alters' roles (e.g., romantic partner, best friend or colleague at work) and the topics of sex-related conversations.
The social network approach has been conceptually and empirically fruitful in research on HIV/ AIDS. It has provided an analytically important counterbalance to the individualized concepts of sexual decision-making, emphasizing the role of social interactions and in-group influences. In the current study, associations between the structural characteristics of ego-centered networks based on conversations about the ego's sex life and HIV-related knowledge, attitudes and behaviours were examined in a population-based sample of young Croatian adults. According to the findings, the associations between the selected HIV-relevant outcomes and the structural properties of social networks were weak and gender-specific. Improving the understanding of likely mechanisms behind network effects seems dependent on collecting more detailed information about the content of network-embedded sexual conversations and on the assessment of the possibility that network influences are not invariant over time.
Acknowledgements: The authors would like to thank Damir Soh and Ivan Puzek for their valuable input during the drafting of the study questionnaire and assistance in the initial phase of data analysis. The research was funded by the Croatian Ministry of Science, Education and Sports, and UNDP Croatia.
Adamczyk, A., & Felson, J. (2006). Friends' religiosity and first sex. Social Science Research, 35, 924-947.
Avogo, W., & Agadjanian, V. (2008). Men's social networks and contraception in Ghana. Journal of Biosocial Science, 40, 413-429.
Bearman, P.S., Moody, J., & Stovel, K. (2004). Chains of affection: The structure of adolescent romantic and sexual networks. American Journal of Sociology, 110, 44-91.
Bearman, P. S., & Parigi, P. (2004). Cloning headless frogs and other important matters: Conversation topics and network structure. Social Forces, 83, 535-557.
Bernardi, L. (2003). Channels of social influence on reproduction. Population Research and Policy Review, 22, 5-6.
Bijelic, N. (2008). Sex education in Croatia: Tensions between secular and religious discourses. European Journal of Women's Studies, 15, 329-343.
Billy, J.O.G., & Udry, J.R. (1985). Patterns of adolescent friendship and effects on sexual behavior. Social Psychology Quarterly, 48, 27-41.
Boyce, P., Huang Soo Lee, M., Jenkins, C., Mohamed, S., Overs, C., Paiva, V., Reid, E., Tan, M. & Aggleton, P. (2007). Putting sexuality (back) into HIV/AIDS: Issues, theory and practice. Global Public Health, 2, 1-34.
Burt, R. (1984). Network items and the general social survey. Social Networks, 6, 293-339.
Choi, K., & Gregorich, S.E. (2009). Social network influences on male and female condom use among women attending family planning clinics in the U.S. Sexually Transmitted Diseases, 36, 757-762.
Christakis, N & Fowler, J. (2009). Connected: The surprising power of our social networks and how they shape our lives. New York, NY: Little, Brown and Company.
European Commission (2011). Croatia 2011 Progress Report. Brussels: SEC(2011) 1200 final, issued 12. 10.2011.
Fichtenberg, C.M., Muth, S.Q., Brown, B., Padian, N. S., Glass, T.A., Ellen, J.M. (2009). Sexual network position and risk of sexually transmitted infections. Sexually Transmitted Infections, 85,493-498.
Ford, K., Sohn, W., & Lepkowski, J. (2001). Characteristics of adolescents' sexual partners and their association with use of condoms and other contraceptive methods. Family Planning Perspectives, 33, 100-105.
Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1, 201-233.
Helleringer, S., & Kohler, H. (2007). Sexual network structure and the spread of HIV in Africa: Evidence from Likoma Island, Malawi. AIDS, 21, 2323-2332.
Kohler, H.-P. (2001). Fertility and social interactions: An economic perspective. New York, NY: Oxford University Press.
Kohler, H.P., Behrman, J.R., & Watkins, S.C. (2001). The density of social networks and fertility decisions: Evidence from South Nyanza district, Kenya. Demography, 38, 43-58.
Landripet, I., Sevic, S., Car, D., Bacak, V., Mamula, M. and Stulhofer, A. (2010). Changing sexuality? Results from repeated cross-sectional studies of the University of Zagreb first-year students, 1998-2008 (in Croatian). Social Research, 19, 995-1014.
Laumann, E.O., Gagnon, J.H., Michael, R.T., & Michaels, S. (1994). The social organization of sexuality. Chicago, IL: University of Chicago Press.
Lefkowitz, E.S., Boone, T.L., & Shearer, C.L. (2004). Communication with best friends about sex-related topics during emerging adulthood. Journal of Youth and Adolescence, 33, 339-351.
Luke, D. & Harris, J. (2007). Network analysis in public health: History, methods, and applications. Annual Review of Public Health, 28, 69-93.
Maxwell K.A. (2002). Friends: The role of peer influence across adolescent risk behaviors. Journal of Youth and Adolescence, 32, 267-277.
McPherson, M., Smith-Lovin, L., and Cook, J. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415-44.
Montgomery, M. & Chung, W.S. (1998). Social networks and the diffusion of fertility control in the Republic of Korea. In R. Leete (Ed.), The dynamics of values in fertility change (pp. 179-209). Oxford, UK: Oxford University Press,.
Morris, M. (Ed.) (2004). Network epidemiology: A handbook for survey design and data collection. New York, NY: Oxford University Press.
Obermayer, C.M. (2004). Reframing research on sexual behavior and HIV. Studies in Family Planning, 36, 1-12.
Puzek, I., Stulhofer, A., & Bozicevic (In press). Is religiosity a barrier to sexual and reproductive health risks? Results from a population-based study of young Croatian adults. Archives of Sexual Behavior.
Rhodes, Y. (1997). Risk theory in epidemic times: Sex, drugs and the social organisation of "risk behaviour". Sociology of Health & Illness, 19, 208-27.
Rhodes, T., Singer, M., Bourgois, P., Friedman, S.R., & Strathdee, S.A. (2005). The social structural production of HIV risk among injecting drug users. Social Science and Medicine, 61, 1026-1044.
Ricardo, C., Barker, G., Pulerwitz, J. & Rocha, V. (2006). Gender, sexual behaviour and vulnerability among young people. In R. Ingham & P. Aggleton (Eds.), Promoting young people's sexual health: International perspectives (pp. 61-78). London, UK: Routledge.
Sieving, R.E., Eisenberg, M.E., Pettingell, S., & Skay, C. (2006). Friends' influence on adolescents' first sexual intercourse. Perspectives on Sexual and Reproductive Health, 38, 13-19.
Smith, K. & Watkins, S.C. (2005). Perceptions of risk and strategies for prevention: Responses to HIV/ AIDS in rural Malawi. Social Science & Medicine, 60, 649-660.
Smith, K.P., & Christakis, N.A. (2008). Social networks and health. Annual Review of Sociology, 34, 405-429.
Stulhofer, A. & Sandfort, T. (Eds.). (2005). Sexuality and gender in post-communist Eastern Europe and Russia. Binghamton, NY: Haworth Press.
Stulhofer, A., Graham, C., Bozicevic, I., Kufrin, K., & Ajdukovic, D. (2007). HIV/AIDS related knowledge, attitudes and sexual behaviors as predictors of condom use in a nationally representative sample of Croatian young adults 18-24. International Family Planning Perspectives, 33, 58-65.
Stulhofer, A., Graham, C., Bozicevic, I., Kufrin, K., & Ajdukovic, D. (2009). An assessment of HIV/STI vulnerability and related sexual risk-taking in a nationally representative sample of young Croatian adults. Archives of Sexual Behavior, 38, 209-25.
Stulhofer, A., Soh, D., Jelaska, N., Badak, V., & Landripet, I. (2010). Religiosity and sexual risk behavior among Croatian college students, 1998-2008. Journal of Sex Research. DOI: 10.1080/00224499.2010.494257.
Traeen, B., Stulhofer, A., & Landripet, I. (2011). Young and sexual in Norway and Croatia: Revisiting the Scandinavian vs. Mediterranean gendered pattern of sexual initiation. International Journal of Sexual Health, 23, 196-209.
Tyler, K.A. (2008). Social network characteristics and risky sexual and drug-related behaviors among homeless young adults. Social Science Research, 37, 673-685.
UNAIDS. (2007). Monitoring the Declaration of Commitment on HIV/AIDS: Guidelines on construction of core indicators--2008 reporting. Geneva: Joint United Nations Programme on HIV/ AIDS (UNAIDS).
Valente, T.W. (2010). Social networks and health: Models, methods, and applications. New York, NY: Oxford University Press.
Valente, T.W., Watkins, S.C., Jato, M.N., van der Straten, A., & Tsitsol, L.P. (1997). Social network associations with contraceptive use among Cameroonian women in voluntary associations. Social Science & Medicine, 45, 677-687.
Watkins, S.C. (2004). Navigating the AIDS epidemic in rural Malawi. Population and Development Review, 30, 673-706.
Jasmina Bozic, Valerio Bacak (1), (2), and Aleksandar Stulhofer (1)
(1) Department of Sociology, Faculty of Humanities and Social Sciences, University of Zagreb, Croatia
(2) Department of Sociology, University of Pennsylvania, Philadelphia, United States
Correspondence concerning this article should be addressed to Jasmina Bozic, Ph. D., Department of Sociology, Faculty of Humanities and Social Sciences, University of Zagreb, I. Lucica 3, 10000 Zagreb, Croatia. E-mail: firstname.lastname@example.org
Table 1 Basic characteristics of personal networks by gender * Men Women n(%) n(%) Network size [less than or equal to] 1 alters 136 (26.7) 118 (23.8) 2 - 3 alters 221 (43.3) 221 (44.6) [greater than or equal to] 4 alters 153 (30.0) 156 (31.5) Network density less than max. number of ties 341 (66.9) 333 (67.3) between alters maximum number of ties 169 (33.1) 162 (32.7) Network religiosity no religious alters 284 (60.4) 239 (50.2) some alters are religious 112 (23.8) 141 (29.6) all alters are religious 74 (15.7) 96 (20.2) Network age homophily alters are younger than ego 204 (40.0) 137 (27.7) alters are of the same age as ego 96 (18.8) 103 (20.8) alters are older than ego 210 (41.2) 255 (51.5) Network gender homophily not all alters are of the 226 (48.1) 217 (45.6) same gender as ego all alters are of the same 244 (51.9) 259 (54.4) gender as ego Network history [less than or equal to] 5 years 136 (28.9) 130 (27.3) of knowing alters 5.1 - 10 years of knowing alters 196 (41.7) 201 (42.2) [greater than or equal to] 10.1 138 (29.4) 145 (30.5) years of knowing alters [chi square] Network size 1.08 [less than or equal to] 1 alters 2 - 3 alters [greater than or equal to] 4 alters Network density 0.02 less than max. number of ties between alters maximum number of ties Network religiosity 10.01 ** no religious alters some alters are religious all alters are religious Network age homophily 17.55 *** alters are younger than ego alters are of the same age as ego alters are older than ego Network gender homophily 0.59 not all alters are of the same gender as ego all alters are of the same gender as ego 0.33 Network history [less than or equal to] 5 years of knowing alters 5.1 - 10 years of knowing alters [greater than or equal to] 10.1 years of knowing alters (a) Due to missing cases not all percentages add up to 100 * p<.05; ** p<.01, *** p<.01 Table 2 Means, analysis of variance and [chi square] for HIV- related knowledge, attitudes and behaviour across social network characteristics by gender HIV knowledge Men Women F M (SD) Network size 3.40 * 0.71 [less than or equal to] 1 5.36 (l.64) 5.46 (l.67) 2-3 5.54 (l.47) 5.49 (1.50) [greater than or equal to] 4 5.13 (l.45) 5.30 (l.47) Network density .21 .00 <. 9 (< max. no. of ties btw. 5.35 (l.52) 5.42 (l.56) alters) max. no. of ties between 5.41 (1.52) 5.42 (l.49) alters Network religiosity 1.08 4.18 * no religious alters 5.45 (l.53) 5.51 (1.57) some alters are religious 5.39 (1.38) 5.49 (l.45) all alters are religious 5.16 (1.54) 5.00 (l.53) Network age homophily 1.81 0.33 alters younger than ego 5.22 (l.56) 5.44 (l.53) alters of the same age as ego 5.56 (l.53) 5.51 (1.41) alters older than ego 5.42 (l.47) 5.37 (l.59) Network gender homophily .14 .63 not all alters of the same gender as ego 5.36 (l.46) 5.34 (l.64) all alters of the same gender as ego 5.41 (1.53) 5.45 (l.45) Network history .19 1.31 [less than or equal to] 5 5.45 (l.52) 5.58 (l.38) years of knowing alters 5.1-10 years of knowing alters 5.38 (l.53) 5.36 (l.60) [greater than or equal to] 5.34 (l.43) 5.30 (l.59) 10.1 years of knowing alters Attitudes toward condom use Men Women F M (SD) Network size 5.69 ** 4.80 ** [less than or equal to] 1 13.15 (4.21) 14.85 (4.18) 2-3 14.36 (3.86) 15.74 (3.51) [greater than or equal to] 4 14.61 (3.77) 16.21 (3.27) Network density .40 5.61 * <. 9 (< max. no. of ties btw. 14.03 (3.95) 15.41 (3.86) alters) max. no. of ties between 14.27 (4.00) 16.23 (3.07) alters Network religiosity 1.97 3.10 * no religious alters 14.54 (3.79) 16.03 (3.53) some alters are religious 14.17 (3.89) 15.96 (3.43) all alters are religious 13.55 (4.20) 15.01 (3.57) Network age homophily .20 .54 alters younger than ego 13.80 (4.07) 15.42 (3.83) alters of the same age as ego 14.50 (4.10) 15.66 (3.82) alters older than ego 14.24 (3.78) 15.82 (3.46) Network gender homophily .72 .68 not all alters of the same gender as ego 14.45 (4.02) 15.66 (3.26) all alters of the same gender as ego 14.15 (3.76) 15.93 (3.73) Network history 2.12 1.64 [less than or equal to] 5 14.44 (3.56) 15.36 (3.56) years of knowing alters 5.1-10 years of knowing alters 14.59 (3.97) 16.07 (3.48) [greater than or equal to] 13.73 (4.03) 15.83 (3.53) 10.1 years of knowing alters HIV risk behaviours Condom use during last intercourse Men Women [chi square] [chi square] yes (%) no (%) yes (%) no (%) Network size 3.80 5.09 * [less than or equal to] 1 41.9 58.1 33.9 66.1 2-3 51.1 48.9 46.6 53.4 [greater than or equal to] 4 52.3 47.7 42.3 57.7 Network density 2.36 .07 <. 9 (< max. no. of ties btw. 46.6 53.4 42.6 57.4 alters) max. no. of ties between 53.8 46.2 41.4 58.6 alters Network religiosity 1.44 1.76 no religious alters 50.4 49.6 43.5 56.5 some alters are religious 53.6 46.4 46.1 53.9 all alters are religious 44.6 55.4 37.5 62.5 Network age homophily 7.82 * 1.60 alters younger than ego 41.7 58.3 38.0 62.0 alters of the same age as ego 51.0 49.0 45.6 54.4 alters older than ego 55.2 44.8 43.1 56.9 Network gender homophily .08 .41 not all alters of the same gender as ego 50.9 49.1 41.5 58.5 all alters of the same gender as ego 49.6 50.4 44.4 55.6 Network history 4.81 * .10 [less than or equal to] 5 58.1 41.9 43.8 56.2 years of knowing alters 5.1-10 years of knowing alters 46.4 53.6 43.3 56.7 [greater than or equal to] 47.8 52.2 42.1 57.9 10.1 years of knowing alters HIV risk behaviours No. of sexual partners in the last 12 months Men [chi square] 0-1(%) [greater than or equal to] 2 (%) Network size 3.84 [less than or equal to] 1 62.7 37.3 2-3 51.1 48.9 [greater than or equal to] 4 52.9 47.1 Network density 2.71 <. 9 (< max. no. of ties btw. 57.3 42.7 alters) max. no. of ties between 49.0 51.0 alters Network religiosity .80 no religious alters 55.6 44.4 some alters are religious 55.4 44.6 all alters are religious 49.1 50.9 Network age homophily .68 alters younger than ego 55.0 45.0 alters of the same age as ego 57.9 42.1 alters older than ego 52.5 47.5 Network gender homophily 2.10 not all alters of the same gender as ego 58.2 41.8 all alters of the same gender as ego 51.0 49.0 Network history 3.90 [less than or equal to] 5 52.5 47.5 years of knowing alters 5.1-10 years of knowing alters 50.9 49.1 [greater than or equal to] 62.2 37.8 10.1 years of knowing alters HIV risk behaviours No. of sexual partners in the last 12 months Women [chi square] 0-1(%) [greater than or equal to] 2 (%) Network size 8.88 * [less than or equal to] 1 88.8 11.2 2-3 73.5 26.5 [greater than or equal to] 4 74.1 25.9 Network density 0.18 <. 9 (< max. no. of ties btw. 77.6 22.4 alters) max. no. of ties between 75.8 24.2 alters Network religiosity 2.92 no religious alters 76.7 23.3 some alters are religious 72.2 27.8 all alters are religious 83.3 16.7 Network age homophily .18 alters younger than ego 75.9 24.1 alters of the same age as ego 76.3 23.7 alters older than ego 77.8 22.2 Network gender homophily .50 not all alters of the same gender as ego 78.0 22.0 all alters of the same gender as ego 75.0 25.0 Network history .70 [less than or equal to] 5 74.0 26.0 years of knowing alters 5.1-10 years of knowing alters 76.4 23.6 [greater than or equal to] 78.8 21.2 10.1 years of knowing alters * p<.05, ** p<.01 Table 3 Socio-demographic and network-based correlates of HIV related knowledge and attitudes by gender HIV knowledge Men (n = 464) F = 3.68 *** [beta] (SE) [beta] Age .05 (.03) .07 Settlement size [less than or equal to] 2,000 = referent 2,001-10,000 -.26 (.20) -.06 10,001-100,000 .19 (.19) .05 [greater than or equal to] 100,001 .06 (.19) .02 Parents' education .24 (.08) .15 ** Sexual experience -.46 (.24) -.09 Frequency of church attendance -.18 (.06) -.15 ** Network size [less than or equal to] 1 = referent 2-3 .03 (.21) .01 [greater than or equal to] 4 -.40 (.22) -.13 Network density <.9 = referent max. no. of ties between alters -.06 (.17) -.02 Network religiosity no religious alters = referent some alters are r. .18 (.18) .05 all alters are r. -.12 (.20) -.03 [R.sup.2] .09 HIV knowledge Women (n = 471) F = 2.16 * [beta] (SE) [beta] Age .05 (.03) .08 Settlement size [less than or equal to] 2,000 = referent 2,001-10,000 -.10 (.21) -.02 10,001-100,000 .00 (.19) .00 [greater than or equal to] 100,001 -.09 (.20) -.02 Parents' education .21 (.07) .14 ** Sexual experience -.36 (.21) -.08 Frequency of church attendance -.04 (.06) -.04 Network size [less than or equal to] 1 = referent 2-3 .00 (.23) .00 [greater than or equal to] 4 -.25 (.23) -.08 Network density <.9 = referent max. no. of ties between alters .02 (.17) .01 Network religiosity no religious alters = referent some alters are r. .09 (.18) .03 all alters are r. -.48 (.21) -.13 * [R.sup.2] .05 Attitudes toward condom use Men (n = 465) F = 1.85 * [beta] (SE) [beta] Age .03 (.08) .02 Settlement size [less than or equal to] 2,000 = referent 2,001-10,000 -.26 (.53) -.03 10,001-100,000 -.25 (.51) -.03 [greater than or equal to] 100,001 -.35 (.50) -.04 Parents' education .35 (.20) .09 Sexual experience .56 (.65) .05 Frequency of church attendance -.44 (.15) -.15 ** Network size [less than or equal to] 1 = referent 2-3 .91 (.57) .12 [greater than or equal to] 4 1.06 (.57) .13 Network density <.9 = referent max. no. of ties between alters -.36 (.44) -.04 Network religiosity no religious alters = referent some alters are r. -.31 (47) -.03 all alters are r. -.20 (.54) -.02 [R.sup.2] .05 Attitudes toward condom use Women (n = 471) F=3.97 *** [beta] (SE) [beta] Age .07 (.07) .05 Settlement size [less than or equal to] 2,000 = referent 2,001-10,000 -.20 (.47) -.02 10,001-100,000 .50 (.43) .06 [greater than or equal to] 100,001 1.14 (.44) .14 * Parents' education .32 (.16) .09 Sexual experience -.13 (.48) -.01 Frequency of church attendance -.46 (.14) -.17 ** Network size [less than or equal to] 1 = referent 2-3 -.33 (.51) -.05 [greater than or equal to] 4 .33 (.51) .04 Network density <.9 = referent max. no. of ties between alters .87 (.38) .12 * Network religiosity no religious alters = referent some alters are r. .18 (.40) .02 all alters are r. -.13 (.47) -.02 [R.sup.2] .09 * p<.05, ** p<.01, *** p<.001 Table 4 Socio-demographic and network-based correlates of HIV-related behaviour by gender Condom use during last intercourse Men Women N=463 N=471 OR (95% CI) OR (95% CI) Age 1.04 (.96-1.14) .98 (.90-1.07) Settlement size [less than or equal to] 1 1 2,000 = referent 2,001-10,000 1.33 (.77-2.32) .66 (.38-1.15) [greater than or equal to] 1.34 (.82-2.38) .70 (.42-1.18) 10,001-100,000 > 100,001 1.13 (.67-1.88) .67 (.40-1.12) Parents' education 1.17 (.94-1.44) 1.25 (1.03-1.5 1) * Frequency of church .96 (.82-1.11) .90 (.77-1.05) attendance Network size [greater than or equal to] 1 1 4 (referent) [less than or equal to] 1 .61 (.34-1.08) .79 (.45-1.38) 2-3 .91 (.59-1.41) 1.27 (.83-1.95) Network age homophily alters older than ego 1 1 (referent) alters younger than ego .58 (.38 -.90) * .95 (.60-1.51) alters of the same age as .97 (.58-1.63) 1.18 (.73-1.91) ego Network history [greater than or equal to] 1 1 10.1 years of knowing alters (referent) [less than or equal to] 5 1.71 (1.01-2.90) * 1.11 (.67-1.83) years of knowing alters 5.1-10 years of knowing .94 (.59-1.48) 1.09 (.70-1.69) alters No. of sexual partners in the last 12 months Men Women N=401 N=391 OR (95% CI) OR (95% CI) Age .88 (.80 -.97) * .91 (.81-1.02) Settlement size [less than or equal to] 1 1 2,000 = referent 2,001-10,000 .94 (.52-1.70) 1.28 (.61-2.67) [greater than or equal to] 1.09 (.61-1.95) 1.39 (.71-2.72) 10,001-100,000 > 100,001 .98 (.56- 1.73) 1.70 (.87-3.31) Parents' education .10 (.79-1.26) .98 (.76-1.27) Frequency of church 1.00 (.85- 1.18) .78 (.64 -.96) * attendance Network size [greater than or equal to] 1 1 4 (referent) [less than or equal to] 1 .55 (.29- 1.04) .43 (.19-1.01) 2-3 1.00 (.63-1.58) 1.08 (.64-1.83) Network age homophily alters older than ego 1 1 (referent) alters younger than ego 1.03 (.64-1.64) 1.31 (.73-2.35) alters of the same age as .75 (.42-1.33) 1.13 (.59-2.15) ego Network history [greater than or equal to] 1 1 10.1 years of knowing alters (referent) [less than or equal to] 5 1.50 (.84-2.68) 1.18 (.61-2.29) years of knowing alters 5.1-10 years of knowing 1.58 (.95-2.61) 1.03 (.58-1.85) alters * p<.05, ** p<.01, *** p<.001
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