Learning science and technology through cooperative education.

Cooperative education, a form of experiential or work‐integrated learning is common in tertiary educational institutions worldwide. However, in New Zealand few institutions provide work‐integrated learning programs in science or technology, and the management and process of work‐integrated learning programs is not that well understood. How well do such programs work? What infrastructure is needed to ensure learning actually occurs? Are graduates of work‐integrated learning programs able to satisfy employer needs? This chapter synthesizes decades of work around such issues, and details research initiatives that provide valuable insights into how students learn science on in the workplace, how their skill development matches that desired by employers, and best practice for management of work‐integrated learning in science and engineering (Asia‐Pacific Journal of Cooperative Education, 2007, 8(2), 131‐147)

Validating the Philadelphia Mindfulness Scale [PMS] for Those with Fibromyalgia

Objectives: Dispositional mindfulness [DM] has become an important construct in understanding and treating fibromyalgia. However, few DM measures exist that have been validated in those with fibromyalgia. The Philadelphia Mindfulness Scale [PMS] is a self-report of DM. In the current study, we validate the PMS within a sample of individuals with fibromyalgia. Design: This was a cross-sectional online study. This enabled the recruitment of a larger sample of individuals with experiences of fibromyalgia than may have been achieved through face-to-face assessment. A cross-sectional approach was adopted to minimise resource demands. Method: The PMS alongside measures of fibromyalgia severity [The Revised Fibromyalgia Impact Questionnaire], affect [Positive and Negative Affect Scale] and decentring [Experiences Questionnaire] were completed online by a sample of N=936 individuals with fibromyalgia. Results: Confirmatory factor analysis supported a revised three-factor structure for the PMS. This factor structure excluded items which could overlap with hypervigilance within fibromyalgia. The three supported factors were Awareness, Non-judging/Control and Non-suppression/reactivity. Concurrent validity of the subscales was partially supported via correlations with positive affect [PA] and negative affect [NA] and decentring. Conclusions: The results support the use of the PMS in individuals with fibromyalgia, and in particular the use of this measure to compare those with and without experience of meditation. The PMS may be a useful tool in evaluating mindfulness-based interventions [MBIs] within this population. Limitations: The online design prevented more in-depth assessment of fibromyalgia. As the study was cross-sectional, test re-test reliability could not be assessed

A motif-based approach to network epidemics

Networks have become an indispensable tool in modelling infectious diseases, with the structure of epidemiologically relevant contacts known to affect both the dynamics of the infection process and the efficacy of intervention strategies. One of the key reasons for this is the presence of clustering in contact networks, which is typically analysed in terms of prevalence of triangles in the network. We present a more general approach, based on the prevalence of different four-motifs, in the context of ODE approximations to network dynamics. This is shown to outperform existing models for a range of small world networks

An exploration of the pedagogies employed to integrate knowledge in work-integrated learning

This article describes a three‐sector, national research project that investigated the integration aspect of work‐integrated learning (WIL). The context for this study is three sectors of New Zealand higher education: business and management, sport, and science and engineering, and a cohort of higher educational institutions that offer WIL/cooperative education in variety of ways. The aims of this study were to investigate the pedagogical approaches in WIL programs that are currently used by WIL practitioners in terms of learning, and the integration of academic‐workplace learning. The research constituted a series of collective case studies, and there were two main data sources — interviews with three stakeholder groups (namely employers, students, and co‐op practitioners), and analyses of relevant documentation (e.g., course/paper outlines, assignments on reflective practice, portfolio of learning, etc.). The research findings suggest that there is no consistent mechanism by which placement coordinators, off‐campus supervisors, or mentors seek to employ or develop pedagogies to foster learning and the integration of knowledge. Learning, it seems, occurs by means of legitimate peripheral participation with off‐campus learning occurring as a result of students working alongside professionals in their area via an apprenticeship model of learning. There is no evidence of explicit attempts to integrate on‐ and off‐campus learning, although all parties felt this would and should occur. However, integration is implicitly or indirectly fostered by a variety of means such as the use of reflective journals

The impact of contact tracing in clustered populations

The tracing of potentially infectious contacts has become an important part of the control strategy for many infectious diseases, from early cases of novel infections to endemic sexually transmitted infections. Here, we make use of mathematical models to consider the case of partner notification for sexually transmitted infection, however these models are sufficiently simple to allow more general conclusions to be drawn. We show that, when contact network structure is considered in addition to contact tracing, standard “mass action” models are generally inadequate. To consider the impact of mutual contacts (specifically clustering) we develop an improvement to existing pairwise network models, which we use to demonstrate that ceteris paribus, clustering improves the efficacy of contact tracing for a large region of parameter space. This result is sometimes reversed, however, for the case of highly effective contact tracing. We also develop stochastic simulations for comparison, using simple re-wiring methods that allow the generation of appropriate comparator networks. In this way we contribute to the general theory of network-based interventions against infectious disease

Edge-Based Compartmental Modeling for Infectious Disease Spread Part III: Disease and Population Structure

We consider the edge-based compartmental models for infectious disease spread introduced in Part I. These models allow us to consider standard SIR diseases spreading in random populations. In this paper we show how to handle deviations of the disease or population from the simplistic assumptions of Part I. We allow the population to have structure due to effects such as demographic detail or multiple types of risk behavior the disease to have more complicated natural history. We introduce these modifications in the static network context, though it is straightforward to incorporate them into dynamic networks. We also consider serosorting, which requires using the dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here

Six challenges in measuring contact networks for use in modelling.

Contact networks are playing an increasingly important role in epidemiology. A contact network represents individuals in a host population as nodes and the interactions among them that may lead to the transmission of infection as edges. New avenues for data collection in recent years have afforded us the opportunity to collect individual- and population-scale information to empirically describe the patterns of contact within host populations. Here, we present some of the current challenges in measuring empirical contact networks. We address fundamental questions such as defining contact; measurement of non-trivial contact properties; practical issues of bounding measurement of contact networks in space, time and scope; exploiting proxy information about contacts; dealing with missing data. Finally, we consider the privacy and ethical issues surrounding the collection of contact network data

School's Out: Seasonal Variation in the Movement Patterns of School Children.

School children are core groups in the transmission of many common infectious diseases, and are likely to play a key role in the spatial dispersal of disease across multiple scales. However, there is currently little detailed information about the spatial movements of this epidemiologically important age group. To address this knowledge gap, we collaborated with eight secondary schools to conduct a survey of movement patterns of school pupils in primary and secondary schools in the United Kingdom. We found evidence of a significant change in behaviour between term time and holidays, with term time weekdays characterised by predominately local movements, and holidays seeing much broader variation in travel patterns. Studies that use mathematical models to examine epidemic transmission and control often use adult commuting data as a proxy for population movements. We show that while these data share some features with the movement patterns reported by school children, there are some crucial differences between the movements of children and adult commuters during both term-time and holidays.AJK was supported by the Medical Research Council (fellowship MR/K021524/1, http://www.mrc.ac.uk/) and the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health (http://www.fic.nih.gov/about/staff/pages​/epidemiology-population.aspx#rapidd). AJKC was supported by the Alborada Trust (http://www.alboradatrust.com/). KTDE was supported by the NIHR (CDF-2011-04- 019, http://www.nihr.ac.uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version. It was first published by PLOS at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0128070#

Characterizing contributions of glacier melt and groundwater during the dry season in a poorly gauged catchment of the Cordillera Blanca (Peru)

The retreat of glaciers in the tropics will have a significant impact on water resources. In order to overcome limitations with discontinuous to nonexistent hydrologic measurements in remote mountain watersheds, a hydrochemical and isotopic mass balance model is used to identify and characterize dry season water origins at the glacier fed Querococha basin located in southern Cordillera Blanca, Peru. Dry season water samples, collected intermittently between 1998 and 2007, were analyzed for major ions and the stable isotopes of water (δ<sup>18</sup>O and δ<sup>2</sup>H). The hydrochemical and isotopic data are analysed using conservative characteristics of selected tracers and relative contributions are calculated based on pre-identified contributing sources at mixing points sampled across the basin. The results show that during the dry-season, groundwater is the largest contributor to basin outflow and that the flux of groundwater is temporally variable. The groundwater contribution significantly correlates (P-value=0.004 to 0.044) to the antecedent precipitation regime at 3 and 18–36 months. Assuming this indicates a maximum of 4 years of precipitation accumulation in groundwater reserves, the Querococha watershed outflows are potentially vulnerable to multi-year droughts and climate related changes in the precipitation regime. The results show that the use of hydrochemical and isotopic data can contribute to hydrologic studies in remote, data poor regions, and that groundwater contribution to tropical proglacial hydrologic systems is a critical component of dry season discharge

Effects of Contact Network Models on Stochastic Epidemic Simulations

The importance of modeling the spread of epidemics through a population has led to the development of mathematical models for infectious disease propagation. A number of empirical studies have collected and analyzed data on contacts between individuals using a variety of sensors. Typically one uses such data to fit a probabilistic model of network contacts over which a disease may propagate. In this paper, we investigate the effects of different contact network models with varying levels of complexity on the outcomes of simulated epidemics using a stochastic Susceptible-Infectious-Recovered (SIR) model. We evaluate these network models on six datasets of contacts between people in a variety of settings. Our results demonstrate that the choice of network model can have a significant effect on how closely the outcomes of an epidemic simulation on a simulated network match the outcomes on the actual network constructed from the sensor data. In particular, preserving degrees of nodes appears to be much more important than preserving cluster structure for accurate epidemic simulations.Comment: To appear at International Conference on Social Informatics (SocInfo) 201