Melbourne School of Psychological Sciences - Research Publications

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    Priority populations' experiences of isolation, quarantine and distancing for COVID-19: protocol for a longitudinal cohort study (Optimise Study)
    Pedrana, A ; Bowring, A ; Heath, K ; Thomas, AJ ; Wilkinson, A ; Fletcher-Lartey, S ; Saich, F ; Munari, S ; Oliver, J ; Merner, B ; Altermatt, A ; Nguyen, T ; Nguyen, L ; Young, K ; Kerr, P ; Osborne, D ; Kwong, EJL ; Corona, MV ; Ke, T ; Zhang, Y ; Eisa, L ; Al-Qassas, A ; Malith, D ; Davis, A ; Gibbs, L ; Block, K ; Horyniak, D ; Wallace, J ; Power, R ; Vadasz, D ; Ryan, R ; Shearer, F ; Homer, C ; Collie, A ; Meagher, N ; Danchin, M ; Kaufman, J ; Wang, P ; Hassani, A ; Sadewo, GRP ; Robins, G ; Gallagher, C ; Matous, P ; Roden, B ; Karkavandi, MA ; Coutinho, J ; Broccatelli, C ; Koskinen, J ; Curtis, S ; Doyle, JS ; Geard, N ; Hill, S ; Coelho, A ; Scott, N ; Lusher, D ; Stoove, MA ; Gibney, KB ; Hellard, M (BMJ PUBLISHING GROUP, 2024-01)
    INTRODUCTION: Longitudinal studies can provide timely and accurate information to evaluate and inform COVID-19 control and mitigation strategies and future pandemic preparedness. The Optimise Study is a multidisciplinary research platform established in the Australian state of Victoria in September 2020 to collect epidemiological, social, psychological and behavioural data from priority populations. It aims to understand changing public attitudes, behaviours and experiences of COVID-19 and inform epidemic modelling and support responsive government policy. METHODS AND ANALYSIS: This protocol paper describes the data collection procedures for the Optimise Study, an ongoing longitudinal cohort of ~1000 Victorian adults and their social networks. Participants are recruited using snowball sampling with a set of seeds and two waves of snowball recruitment. Seeds are purposively selected from priority groups, including recent COVID-19 cases and close contacts and people at heightened risk of infection and/or adverse outcomes of COVID-19 infection and/or public health measures. Participants complete a schedule of monthly quantitative surveys and daily diaries for up to 24 months, plus additional surveys annually for up to 48 months. Cohort participants are recruited for qualitative interviews at key time points to enable in-depth exploration of people's lived experiences. Separately, community representatives are invited to participate in community engagement groups, which review and interpret research findings to inform policy and practice recommendations. ETHICS AND DISSEMINATION: The Optimise longitudinal cohort and qualitative interviews are approved by the Alfred Hospital Human Research Ethics Committee (# 333/20). The Optimise Study CEG is approved by the La Trobe University Human Ethics Committee (# HEC20532). All participants provide informed verbal consent to enter the cohort, with additional consent provided prior to any of the sub studies. Study findings will be disseminated through public website (https://optimisecovid.com.au/study-findings/) and through peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT05323799.
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    Perceived friendship network of socially anxious adolescent girls
    Karkavandi, MA ; Wang, P ; Lusher, D ; Bastian, B ; McKenzie, V ; Robins, G (ELSEVIER, 2022-01)
    In this study we investigated how social anxiety and expressed friendship relate to the perceived friendship network of adolescent girls. We define an expressed friendship as a friendship choice made by an individual, and a perceived friend as one whom the individual perceives as choosing them as a friend; expressed and perceived friends need not be the same. We applied Exponential Random Graph Models (ERGMs) to understand effects of social anxiety and expressed friendship on structural patterns of the perceived friendship network, aiming to shed light how social anxiety and expressed friendship choices relate to cognitive perceptions of the social environment (in terms of perceived friendship partners). Participants were 94 year-nine students recruited from an all-female high school in Melbourne, Australia. Results indicated that socially anxious students were similar to other students in terms of their number of perceived friends but were themselves less popular in the perceived friendship network. High anxiety students perceive friendship from low anxiety alters. Expressed and perceived friendship ties tended to be congruent so that students expressed friendship to partners who they perceived would nominate them as friends. Furthermore, students tended to be accurate in understanding their social environment in that perceived friends did tend to nominate them. Socially anxious students did not differ markedly from other students in terms of congruence and accuracy, although congruence was less likely in in ties from high anxiety to low anxiety students. The results indicate the ways in which social anxiety influences how students perceive their friendship relations.
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    Reconciling Conflict and Cooperation in Environmental Governance: A Social Network Perspective
    Bodin, O ; Garcia, MM ; Robins, G ; Gadgil, A ; Tomich, TP (ANNUAL REVIEWS, 2020)
    Most if not all environmental problems entail conflicts of interest. Yet, different actors and opposing coalitions often but certainly not always cooperate in solving these problems. Hence, processes of conflict and cooperation often work in tandem, albeit much of the scholarly literature tends to focus on either of these phenomena in isolation. Social network analysis (SNA) provides opportunities to study cooperation and conflict together. In this review, we demonstrate how SNA has increased our understanding of the promises and pitfalls of collaborative approaches in addressing environmental problems. The potential of SNA to investigate conflicts in environmental governance, however, remains largely underutilized. Furthermore, a network perspective is not restricted to the social domain. A multilevel social-ecological network perspective facilitates integration of social and environmental sciences in understanding how different patterns of resource access can trigger both cooperation and conflict.
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    Low carbon readiness in social context: Introducing the social context of environmental identity model
    Kashima, Y ; O'Brien, L ; McNeill, I ; Ambrose, M ; Bruce, G ; Critchley, CR ; Dudgeon, P ; Newton, P ; Robins, G (WILEY, 2021-06)
    Low carbon readiness (LCR) is an aspect of environmental identity, an individual citizen’s willingness to reduce carbon emissions and transition to low carbon lifestyle as a personal striving. Nevertheless, individuals’ personal strivings are strongly influenced by the social context in which they are situated. We propose the social context of environmental identity model, which postulates that social contexts for LCR have a nested structure. The micro‐level Home is linked with other households through social networks at the meso‐level Community, which are further embedded in a macro‐level Society. These contexts are likely to influence LCR through different mechanisms. Home can exert direct influences by monitoring and reminding each other of the need to engage in low carbon behaviours. Community affects individuals’ readiness by providing social capital. The macro‐level Society exerts social influence through societal norms not only its current descriptive norm but also through its dynamic norms about the changing trends into the future. We have tested and found support for these propositions in three national cross‐sectional data sets from Australia. Our discussions will centre around a need to investigate social and cultural processes involved in climate change mitigation, and to link these insights to public policies.
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    Exponential random graph model parameter estimation for very large directed networks
    Stivala, A ; Robins, G ; Lomi, A ; Mariño, IP (PUBLIC LIBRARY SCIENCE, 2020-01-24)
    Exponential random graph models (ERGMs) are widely used for modeling social networks observed at one point in time. However the computational difficulty of ERGM parameter estimation has limited the practical application of this class of models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.