Doctoral supervision in virtual spaces: A review of research of web-based tools to develop collaborative supervision

Supervision of doctoral students needs to be improved to increase completion rates, reduce attrition rates (estimated to be at 25% or more) and improve quality of research. The current literature review aimed to explore the contribution that technology can make to higher degree research supervision. The articles selected included empirical studies that sought to improve supervision through the use of technology. The literature search focused on technology, supervision and pedagogical supervision, and supervisor–supervisee relationships. Eighteen empirical articles, including Web 2.0 settings, were examined in relation to whether web-based tools could influence the training of doctoral students, be effective in supporting students, and reduce the breakdowns in supervisory relationships. With a few exceptions, these studies showed that Web 2.0 tools enabled greater dialogue and interaction between the student and supervisor rather than a passive viewing of content. They created virtual spaces that combined technology and pedagogy into a process where research projects could be developed in a more collegial and collaborative way. It appeared that combining technology with pedagogy translated into more innovative ways to undertake supervision, particularly participatory supervision. The need for digital pedagogies that facilitate multidimensional changes in higher degree supervision was identified for future research

Does executive function mediate the path from mothers' depressive symptoms to young children's problem behaviors?

This study investigated the mediation role played by children's executive function in the relationship between exposure to mild maternal depressive symptoms and problem behaviors. At ages 2, 3, and 6years, 143 children completed executive function tasks and a verbal ability test. Mothers completed the Beck Depression Inventory at each time-point, and teachers completed the Strengths and Difficulties Questionnaire at child age 6. Longitudinal autoregressive mediation models showed a mediation effect that was significant and quite specific; executive function (and not verbal ability) at age 3 mediated the path between mothers' depressive symptoms (but not general social disadvantage) at the first time-point and children's externalizing and internalizing problems at age 6. Improving children's executive functioning might protect them against the adverse effects of exposure to maternal depressive symptoms.This research was funded by a grant to Claire Hughes from the Economic and Social Research Council (ref: ES/D00554X/1). We thank the families who participated in the Toddlers Up study.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.jecp.2015.09.02

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis)

What factors affect patients’ access to healthcare? Protocol for an overview of systematic reviews

Background The importance of access to healthcare for all is internationally recognised as a global goal, high on the global agenda. Yet inequalities in health exist within and between countries which are exacerbated by inequalities in access to healthcare. In order to address these inequalities, we need to better understand what drives them. While there exists a wealth of research on access to healthcare in different countries and contexts, and for different patient groups, to date no attempt has been made to bring this evidence together through a global lens. This study aims to address that gap by bringing together evidence of what factors affect patients’ access to healthcare and exploring how those factors vary in different countries and contexts around the world. Methods An overview of reviews will be conducted using a comprehensive search strategy to search four databases: Medline, Embase, Global Health and Cochrane Systematic Reviews. Additional searches will be conducted on the Gates Foundation, the World Health Organisation (WHO) and World Bank websites. Titles and abstracts will be screened against the eligibility criteria and full-text articles will be obtained for all records that meet the inclusion criteria or where there is uncertainty around eligibility. A data extraction table will be developed during the review process and will be piloted and refined before full data extraction commences. Methodological quality/risk of bias will be assessed for each included study using the AMSTAR 2 tool. The quality assessment will be used to inform the narrative synthesis, but a low-quality score will not necessarily lead to study exclusion. Discussion Factors affecting patients’ ability to access healthcare will be identified and analysed according to different country and context characteristics to shed light on the importance of different factors in different settings. Results will be interpreted accounting for the usual challenges associated with conducting such reviews. The results may guide future research in this area and contribute to priority setting for development initiatives aimed at ensuring equitable access to healthcare for all

Contribution of Nepal’s Free Delivery Care Policies in Improving Utilisation of Maternal Health Services

Background: Nepal has made remarkable improvements in maternal health outcomes. The implementation of demand and supply side strategies have often been attributed with the observed increase in utilization of maternal healthcare services. In 2005, Free Delivery Care (FDC) policy was implemented under the name of Maternity Incentive Scheme (MIS), with the intention of reducing transport costs associated with giving birth in a health facility. In 2009, MIS was expanded to include free delivery services. The new expanded programme was named “Aama” programme, and further provided a cash incentive for attending four or more antenatal visits. This article analysed the influence of FDC policies, individual and community level factors in the utilisation of four antenatal care (4 ANC) visits and institutional deliveries in Nepal. Methods: Demographic and health survey data from 1996, 2001, 2006 and 2011 were used and a multi-level analysis was employed to determine the effect of FDC policy intervention, individual and community level factors in utilisation of 4 ANC visits and institutional delivery services. Results: Multivariate analysis suggests that FDC policy had the largest effect in the utilisation of 4 ANC visits and institutional delivery compared to individual and community factors. After the implementation of MIS in 2005, women were three times (adjusted odds ratio [AOR]=3.020, P<.001) more likely to attend 4 ANC visits than when there was no FDC policy. After the implementation of Aama programme in 2009, the likelihood of attending 4 ANC visits increased six-folds (AOR=6.006, P<.001) compared prior to the implementation of FDC policy. Similarly, institutional deliveries increased two times after the implementation of the MIS (AOR=2.117, P<.001) than when there was no FDC policy. The institutional deliveries increased five-folds (AOR=5.116, P<.001) after the implementation of Aama compared to no FDC policy. Conclusion: Results from this study suggest that MIS and Aama policies have had a strong positive influence on the utilisation of 4 ANC visits and institutional deliveries in Nepal. Nevertheless, results also show that FDC policies may not be sufficient in raising demand for maternal health services withoutadequately considering the individual and community level factors

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis)

Alternative approach for the evaluation of road pricing strategies

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (p. 197-205).Interest in road pricing among political leaders, transportation analysts, academics, and government agencies has increased in recent years. There are myriad reasons for this newfound consideration, but the deployment of intelligent transportation systems, the desire for additional revenue sources, and the search for policies that can reduce congestion are among the most important. This thesis examines the impacts of six different types of road pricing strategies, namely: conventional tolling, facility congestion pricing, express lanes (e.g. HOT lanes), area- wide and cordon pricing, network pricing, and distance-based pricing. It also presents a new sketch-planning model, the Road Pricing Decision Analysis Tool (RPDAT), which highlights each strategy's unique set of strengths and weaknesses for achieving different policy objectives and recommends road pricing strategies for particular metropolitan areas. Despite a growing interest in pricing, many decision makers feel unable to estimate the impacts of pricing strategies accurately with conventional models. This thesis discusses the factors believed to be responsible for drivers' choosing to use priced facilities, explains why conventional models are incapable of capturing many of these factors or the aggregate effects of a pricing policy, and identifies some improvements that could be made to existing transportation models. RPDAT performs a multi-criteria analysis of nine road pricing strategies, one of which is a "no pricing" alternative, for a metropolitan area. The user inputs policy priorities and regional characteristics, and RPDAT's algorithms calculate how well each alternative meets the decision maker's criteria as well as index scores that reflect the overall preference for each alternative. This tool is applied to Kuala Lumpur (KL), Malaysia and is used to recommend road pricing strategies for the KL metropolitan area.by Jeffrey D. Ensor.S.M

Calculating the power of a planned individual participant data meta‐analysis of randomised trials to examine a treatment‐covariate interaction with a time‐to‐event outcome

Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers should consider the power of their planned IPDMA conditional on the studies promising their IPD and their characteristics. Such power estimates help inform whether the IPDMA project is worth the time and funding investment, before IPD are collected. Here, we suggest how to estimate the power of a planned IPDMA of randomised trials aiming to examine treatment-covariate interactions at the participant-level (i.e., treatment effect modifiers). We focus on a time-to-event (survival) outcome with a binary or continuous covariate, and propose an approximate analytic power calculation that conditions on the actual characteristics of trials, for example, in terms of sample sizes and covariate distributions. The proposed method has five steps: (i) extracting the following aggregate data for each group in each trial—the number of participants and events, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate; (ii) specifying a minimally important interaction size; (iii) deriving an approximate estimate of Fisher's information matrix for each trial and the corresponding variance of the interaction estimate per trial, based on assuming an exponential survival distribution; (iv) deriving the estimated variance of the summary interaction estimate from the planned IPDMA, under a common-effect assumption, and (v) calculating the power of the IPDMA based on a two-sided Wald test. Stata and R code are provided and a real example provided for illustration. Further evaluation in real examples and simulations is needed

Two-stage or not two-stage? That is the question for IPD meta-analysis projects

Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g., treatment effect estimates and standard errors); then, in the second stage, these aggregate data are combined in a standard meta-analysis model (e.g., common-effect or random-effects). In a one-stage approach, the IPD from all studies are analysed in a single step using an appropriate model that accounts for clustering of participants within studies and, potentially, between-study heterogeneity (e.g., a general or generalised linear mixed model). The best approach to take is debated in the literature, and so here we provide clearer guidance for a broad audience. Both approaches are important tools for IPDMA researchers and neither are a panacea. If most studies in the IPDMA are small (few participants or events), a one-stage approach is recommended due to using a more exact likelihood. However, in other situations, researchers can choose either approach, carefully following best practice. Some previous claims recommending to always use a one-stage approach are misleading, and the two-stage approach will often suffice for most researchers. When differences do arise between the two approaches, often it is caused by researchers using different modelling assumptions or estimation methods, rather than using one or two stages per se