Browsing by Subject "social network analysis"

Now showing 1 - 3 of 3
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    Open Access
    Investigating the impact of a parenting intervention within a rural South African community: a longitudinal social network analysis
    (2021) Kleyn, Lisa Marguerite; Ward, Catherine L; Hewstone, Miles C; Wölfer, Ralf
    Colder, harsher parenting attitudes and behaviours negatively impact children's behaviour and development, and have been linked to heightened levels of violence towards children. Parenting interventions can improve outcomes by reducing violent and increasing non-violent parenting behaviours. I investigated how changes associated with a low-cost positive parenting intervention spread through a rural, low-income, South African community. Specifically, I assessed whether exposure to a community-wide social activation process and Parenting for Lifelong Health (PLH) programmes (focused on violence prevention in low-resource settings) significantly predict: (1) improved parenting, and (2) change in the communication networks of female caregivers in the whole community, while controlling for variables such as psychiatric symptoms, parenting stress, and alcohol misuse. Additionally, I investigated whether ties to parenting programme attendees in the communication network predicted improved parenting. Afrikaans-speaking female caregivers (n = 235; mean age 35.92 years), with children aged between 1½ and 18 years old, participated in the intervention; three waves of data were collected (January 2016, June 2017, and February 2019). The social network was measured based on a peer nomination procedure (of study participants whom “you talk to about parenting”). To analyse the role of interpersonal ties as pathways for spreading intervention effects, I make use of Social Network Analysis (SNA), in the form of nominations of people with whom respondents discuss parenting, together with self-report measures of parenting-related outcomes (from caregivers and their children). I then trace the extent to which both the social activation process and the parenting programmes are effective, in part, via their diffusion throughout the community. SNA was used to disentangle whether network changes improved parenting practices (i.e., selection effects) or whether reported improvements in parenting practices improved caregiver information networks (i.e., socialisation effects). Analysis of data from waves 1 and 2 indicated that community-wide improvements in parenting behaviour were evidenced. The significant predictors of improvement were social activation “dose” received, change in network centrality and the influence of indirect exposure to the parenting programmes via attendees. Furthermore, attending at least one session of a parenting programme offered in the intervention significantly predicted change in the caregivers' communication networks, indicating the spread of social influence through their network. The small subset of caregivers (n = 51; 21.7%) attending one or more sessions of a parenting programme evidenced greater activity (i.e., covariate ego effect) and potential influence (i.e., covariate alter effect) within the communication network compared to caregivers who did not attend any programme sessions. This subset of attending caregivers were more likely to reach out to other caregivers to speak about parenting after being exposed to the intervention, and both sought and received social support from other caregivers. Follow-up assessment using a third wave of data showed that while attendees remained socially influential within the caregiver network the overall community improvement was not sustained. These results illustrate the value of social network analysis for ascertaining the pathways through which the intervention achieved its impact and tracking the evolution of social norms within a community. The results indicate an association between spill-over effects from attendees to non-attendees and community-wide changes through targeted interventions.
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    Open Access
    The medical device development landscape in South Africa: Institutions, sectors and collaboration
    (2017) de Jager, Kylie; Chimhundu, Chipo; Saidi, Trust; Douglas, Tania S
    A characterisation of the medical device development landscape in South Africa would be beneficial for future policy developments that encourage locally developed devices to address local healthcare needs. The landscape was explored through a bibliometric analysis (2000–2013) of relevant scientific papers using co-authorship as an indicator of collaboration. Collaborating institutions thus found were divided into four sectors: academia (A); healthcare (H); industry (I); and science and support (S). A collaboration network was drawn to show the links between the institutions and analysed using network analysis metrics. Centrality measures identified seven dominant local institutions from three sectors. Group densities were used to quantify the extent of collaboration: the A sector collaborated the most extensively both within and between sectors; local collaborations were more prevalent than international collaborations. Translational collaborations (AHI, HIS or AHIS) are considered to be pivotal in fostering medical device innovation that is both relevant and likely to be commercialised. Few such collaborations were found, suggesting room for increased collaboration of these types in South Africa.
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    Understanding pathogen transmission dynamics in waterbird communities: At what scale should interactions be studied?
    (Academy of Science of South Africa, 2011) MacGregor, Lindy H; Cumming, Graeme S; Hockey, Philip A
    Pathogen transmission in animal populations is contingent on interactions between and within species. Often standard ornithological data (e.g. total counts at a wetland) are the only data available for assessing the risks of avian pathogen transmission. In this paper we ask whether these data can be used to infer fine-scale transmission patterns. We tested for non-randomness in waterbird assemblages and explored waterbird interactions using social network analysis. Certain network parameter values were then compared to a data set on avian influenza prevalence in southern Africa. Our results showed that species associations were strongly non-random, implying that most standard ornithological data sets would not provide adequate information on which to base models of pathogen spread. In both aquatic and terrestrial networks, all species regularly associated closely with other network members. The spread of pathogens through the community could thus be rapid. Network analysis together with detailed, fine-scale observations offers a promising avenue for further research and management-oriented applications.