Learning relationships in community-based service-learning: a social network analysis.

BACKGROUND: Little is known about the social learning of students within community-based clinical placements and ways in which it can be supported. In an allied health service-learning program, we analysed students' learning relationships to quantify what, and from whom students learnt. METHODS: We conducted a social learning network survey in four domains of learning (clinical knowledge, procedural skills, professional development, and complex determinants of health) to explore learning relationships (ties) with other people (alters) that students (egos) formed during their placement. We quantified how different roles (supervisors, health professionals, administrators, peers, schoolteachers, and clients) contributed to the students' learning in each of the four domains. We used exponential random graph models (ERGMs) to test which relational processes contributed to the structure of the observed learning networks. RESULTS: Data was available from a complete cohort of 10 students on placement in a network of 69 members, thus providing information on 680 potential learning relations. Students engaged in similar ways in the domains of clinical knowledge, procedural skills, and professional development. Learning relations with academic supervisors were significantly more likely. Also students reported reciprocal learning relations with peers - i.e. they formed learning pairs. This effect was absent in learning networks about complex determinants of health (including socio-economic and cultural factors). Instead, local administrative staff were significantly more often the source of learning about the local contextual factors. CONCLUSIONS: Understanding the structure of student learning networks through social network analysis helps identify targeted strategies to enhance learning in community-based service-learning programs. Our findings suggest students recognised important learning from each other and from administrative personnel that is unrelated to the content of their placement. Based on this insight clinical educators could prepare students to become agentic learners, learning with each other and from sources outside their program.

Growing health partnerships in rural and remote communities: what drives the joint efforts of primary schools and universities in maintaining service learning partnerships?

AimThis study explored the partnership between universities and local primary schools to deliver a classroom-based paediatric communication impairment service provided by undergraduate speech pathology students. It aimed to understand how partnerships work to facilitate programme replication. BACKGROUND: The partners included universities sending students on rural clinical placement, local host academic units and primary schools who worked together to provide paediatric speech and language services in primary schools in three sites in Australia. Rural and remote communities experience poorer health outcomes because of chronic workforce shortages, social disadvantage and high Aboriginality, poor access to services and underfunding. METHODS: The study was in twofold: qualitative analysis of data from interviews/focus group with the partners in the university and education sectors, and quantitative social network analysis of data from an electronic survey of the partners.FindingsFactors supporting partnerships were long-term, work and social relationships, commitment to community, trust and an appetite for risk-taking. We postulate that these characteristics are more likely to exist in rural communities.

Tracing the Potential Flow of Consumer Data: A Network Analysis of Prominent Health and Fitness Apps.

BACKGROUND: A great deal of consumer data, collected actively through consumer reporting or passively through sensors, is shared among apps. Developers increasingly allow their programs to communicate with other apps, sensors, and Web-based services, which are promoted as features to potential users. However, health apps also routinely pose risks related to information leaks, information manipulation, and loss of information. There has been less investigation into the kinds of user data that developers are likely to collect, and who might have access to it. OBJECTIVE: We sought to describe how consumer data generated from mobile health apps might be distributed and reused. We also aimed to outline risks to individual privacy and security presented by this potential for aggregating and combining user data across apps. METHODS: We purposively sampled prominent health and fitness apps available in the United States, Canada, and Australia Google Play and iTunes app stores in November 2015. Two independent coders extracted data from app promotional materials on app and developer characteristics, and the developer-reported collection and sharing of user data. We conducted a descriptive analysis of app, developer, and user data collection characteristics. Using structural equivalence analysis, we conducted a network analysis of sampled apps' self-reported sharing of user-generated data. RESULTS: We included 297 unique apps published by 231 individual developers, which requested 58 different permissions (mean 7.95, SD 6.57). We grouped apps into 222 app families on the basis of shared ownership. Analysis of self-reported data sharing revealed a network of 359 app family nodes, with one connected central component of 210 app families (58.5%). Most (143/222, 64.4%) of the sampled app families did not report sharing any data and were therefore isolated from each other and from the core network. Fifteen app families assumed more central network positions as gatekeepers on the shortest paths that data would have to travel between other app families. CONCLUSIONS: This cross-sectional analysis highlights the possibilities for user data collection and potential paths that data is able to travel among a sample of prominent health and fitness apps. While individual apps may not collect personally identifiable information, app families and the partners with which they share data may be able to aggregate consumer data, thus achieving a much more comprehensive picture of the individual consumer. The organizations behind the centrally connected app families represent diverse industries, including apparel manufacturers and social media platforms that are not traditionally involved in health or fitness. This analysis highlights the potential for anticipated and voluntary but also possibly unanticipated and involuntary sharing of user data, validating privacy and security concerns in mobile health.

Association Rules Analysis of Comorbidity and Multimorbidity: The Concord Health and Aging in Men Project.

BACKGROUND: Comorbidity and multimorbidity are common in older people. Here we used a novel analytic approach called Association Rules together with network analysis to evaluate multimorbidity (two or more disorders) and comorbidity in old age. METHODS: A population-based cross-sectional study was undertaken where 17 morbidities were analyzed using network analysis, cluster analysis, and Association Rules methodology. A comorbidity interestingness score was developed to quantify the richness and variability of comorbidities associated with an index condition. The participants were community-dwelling men aged 70 years or older from the Concord Health and Ageing in Men Project, Sydney, Australia, with complete data (n = 1,464). RESULTS: The vast majority (75%) of participants had multimorbidity. Several morbidity clusters were apparent (vascular cluster, metabolic cluster, neurodegenerative cluster, mental health and other cluster, and a musculoskeletal and other cluster). Association Rules revealed unexpected comorbidities with high lift and confidence linked to index diseases. Anxiety and heart failure had the highest comorbidity interestingness scores while obesity, hearing impairment, and arthritis had the lowest (zero) scores. We also performed Association Rules analysis for the geriatric syndromes of frailty and falls to determine their association with multimorbidity. Frailty had a very complex and rich set of frequent and interesting comorbidities, while there were no frequent and interesting sets associated with falls. CONCLUSIONS: Old age is characterized by a complex pattern of multimorbidity and comorbidity. Single disease definitions do not account for the prevalence and complexity of multimorbidity in older people and a new lexicon may be needed to underpin research and health care interventions for older people.