Table 2 Key findings
From: Social networks and female reproductive choices in the developing world: a systematized review
Author(s) (Date) | Context | Study design | Sample size | Social network collection methods | Social network analysis methods | Variables | Key Findings in Relation to Mechanisms | p- value |
|---|---|---|---|---|---|---|---|---|
Billari, Philipov & Testa (2009) [21] | Bulgaria | Cross-sectional | Men and women: 10,003, ages 18-34. | Name generator | Logistic regression models | Intentions to have a first and second child; attitudes, norms and perceived behavioral control related to fertility behavior. | Normative pressure, or the “perception of social influence” affected reproductive behavior, changing from being in favor of high childbirth rates to pro-contraception. | Norms; P = 0.00 |
Bove, Vala-Haynes, & Valeggia (2012) [5] | Mali | Cross-sectional | 324 women, ages 15-80 | Number of individuals a respondent identified. | Logistic and linear regression models | Pregnancy histories, women’s knowledge of contraception, and illness symptoms in the past three months and the treatment (if sought, financing and sources of social support). | Social influence in larger social networks resulted in increased pregnancy in the previous two years, associated with a larger social network. | Larger social network associated with increased odds of pregnancy during the previous 2 years, p < .01 |
Dynes, Stephenson, Rubardt & Bartel (2012) [4] | Ethiopia and Kenya | Cross-sectional | Ethiopia: 520 women; 300 men | Random generator. | Logistic regression model | Perceptions of current norms and community norms on current contraceptive use | Perceptions of social norms influenced reproductive behavior, including son preference and contraceptive use. | difference between women’s perception of the community ideal number of sons and their own actual number of sons is negatively associated with contraceptive use (Ethiopia OR 0.74, 95% CI 0.61–0.89; Kenya OR 0.77, 95% CI 0.66–0.89). a higher score on the family planning perception of other’s approval index was significantly associated with current contraceptive use among men and women in Kenya (OR 2.03, 95% CI 1.35–3.05 and OR 1.36, 95% CI 1.06–1.74, respectively); this association, however, was not present among samples in Ethiopia. |
Kenya: 655 women; 310 men | ||||||||
Edmonds, Hruschka, Bernard, & Sibley (2012) [19] | Bangladesh | Cross-sectional | 246 women, 18-49 years. | Network generator and network characteristics. | Logistic regression models | Place of delivery, whether home or facility | The collective advice of others, or social influence, whether correctly perceived or not, affected birth decisions of women. | Skilled Birth Attendant Endorsement by network p = .000 |
Gayen & Raeside (2007) [18] | Bangladesh | Cross-sectional | 694 women who had at least one child, | Name generator. | Logistic regression models | Experience of neonatal death and choice of assistance for delivery | Social influence impacts choice in type of assistance while giving birth. | Degree centrality in relation to unqualified assistance P = 0.00; degree centrality in relation to professional assistance p = .01. |
Gayen & Raeside (2010) [20] | Bangladesh | Cross-sectional | 694 women currently married of reproductive age | Name generator. | Logistic regression models | Current use of contraception | Both social learning and social influence impacted family planning decisions. | Network members’ approval of contraception, p < 0.05. Network members’ encouragement to use contraception, p < 0.05. Discussion frequency on contraception with network members, p < 0.05. |
Kincaid (2000) [14] | Bangladesh | Longitudinal | 860 married women, age 14-49. | Random generator. | Logistic regression model; Conditional (static-score) multiple regression analysis | Modern contraceptive use | A social network approach, specifically group discussions in key opinion leader’s homes, allowed for increased social influence to accelerate rate of change concerning contraceptive use. | Social network approach change in ideation p < 0.001, change in contraceptive use, p < 0.001. |
Kohler, Behrman & Watkins (2001) [24] | Kenya | Longitudinal | 694 women currently married | Name generator | Logistic regression; Measures of network density. | Family planning use | More heterogeneous groups with high amounts of activity were dominated by social learning and more homogenous groups are dominated by social influence. | In low-density Owich, Kawadhgone and Wakula South, the % users influence on family planning is p < 0.01; In high-density Obisa, density influences family planning p < 0.01. |
Lindstrom & Munoz-Franco (2005) [23] | Guatemala | Cross-sectional | 2871 women, age 18-35. | Random generator. | Multilevel logistic regression model | Contraceptive knowledge | Social learning is integral in areas where networks increased in heterogeneity. Key actors also influenced this learning. | Migration experience, family migration networks, and community urban out-migrant networks were statistically significant at precdicting the number of modern contraceptive methods known, p < 0.05. |
Madhavan, Adams & Simon (2003) [13] | Mali | Cross-sectional | 502 women, aged 15-45, | Random generator | Ordinary least-squares regression; logistic regression | Two fertility-related outcomes – completed fertility and contraceptive use | Homogenous networks facilitated social influence as a mechanism for diffusion; ‘gatekeepers’ generally dictated these societal norms and had more influence than others. | Ever use of contraceptives contraceptives: Presence of mother P < 0.05; % of network who are natal kin, p < 0.05; $ of network who are conjugal kin p < 0.01; % of network who live outside villag, p < 0.001. |
Musalia (2003) [25] | Kenya | Cross-sectional | 200 to 323 women, younger than 50 | Name generator. | Logistic regression analysis. | Educational heterogeneity; membership in voluntary organization; network size; contraception use. | Social influence of kin groups affected spousal discussion of contraceptive use, but as gave way to social learning as new ideas were embraced. | Being a member of a social group: Kakamega, p < 0.05; Murang’a, p < 0.01. |
Musalia (2005) [26] | Kenya | Cross-sectional | 557 women and 536 men | Name generator. | Logistic regression analysis | Ever use of contraception and current use of contraception. | Social influence both hindered and helped the adoption of reproductive behaviors | Current use of contracetion, ntowrk advices use of family planning, p < 0.01; ever use of contraception, network advices use of family planning, p < 0.01. |
Sandberg (2005) [29] | Nepal | Cross-sectional | 77 currently married women, younger than 50 | Name generator | Logistic-regression | Desiring more children. | Social learning and collective social experiences influenced actor decisions and behaviors. | Desiring more children impacted by network infant mortality, p < 0.05; and any child died in last birth interval, p < 0.01. |
Valente, Watkins, Jato, Van Der Straten, & Tsitsol (1997) [27] | Cameroon | Cross-sectional | 495 women, under the age of 45 | Name generator. | Use logit-regression models. | Whether respondent ever-used a contraceptive, a clinic-based method, and a non-clinic based method. | Social influence and social learning were important within networks, though influence is heightened within associations due to encouragement between members. | Perceived approval of contraction, have used contraception, have encouraged network partners to use all p < 0.0001. |
Behrman, Kohler & Watkins (2002) [16] | Kenya | Longitudinal | 497 women; 324 men | Name generator. | Logit model | Whether a respondent was currently using contraception (at the time of the survey). | Social learning was the primary means of transmitting information through a network. | At least one family planning uer in the network p < 0.05; Number of remaining family planning users in the network, p < 0.01. |
Boulay & Valente (1999) [22] | Kenya | Cross-sectional | 2,217 women, aged 15-49; 2,152 men, aged 15-54 | Random generator. | Logistic Regression models | Family planning knowledge, attitudes, and practices. | Extended social networks led to high amounts of transmission of family planning information passed through community groups. | Family planning knowledge, approval, use and discussion among members of clubs: know 5 modern methods, p < 0.001, and talked about family planning with anyone p < 0.01, with core network only, p < 0.05, and with core and extended networks, p < 0.001. |
Valente & Saba (1998) [17] | Bolivia | Longitudinal | First sample: 2300 youngest men and women present in household; Second sample:800 residents in Potosi. | Name generator. | Regression model with demographic controls. | Family planning awareness; reproductive health knowledge; reproductive health attitudes; family planning intention; interpersonal communication; current use of contraceptives. | Social learning in the form of mass media campaigns were associated with behavior change for individuals who have networks with low amounts of contraceptive use. | Network exposure and current use of contraception (p < 0.01) was associated with family planning awareness p < 0.01, reproductive health knowledge p < 0.01, reproductive health attitude p < 0.01, family planning intention p < 0.01, |
Godley (2001) [28] | Thailand | Cross-Sectional | 1,563 women aged 18-35 who had been married 10 years or less. | Random generator. | Logistic regression models; multilevel networks | Choice in contraceptive. | The specific social network of extended kin influenced contraceptive choice both through both social learning and social influence. | Method choice without television with p < 0.05, and method choice with television, p < 0.05. |