Evaluating the 'Parental Understanding of Neurodisability Questionnaire' as a measure of change

This thesis aimed to provide a second validation phase of a recently developed measure: the Parental Understanding of Neurodisability Questionnaire (PUN-Q) (Moran et al., submitted). The PUN-Q is a thirteen item self-report questionnaire measuring parents’ understanding of their child’s neurodisability. This thesis prospectively validated the PUN-Q over three time points, prior to and following a child’s attendance at a Tier-Four paediatric diagnostic assessment, for queries regarding social communication. Four main aims were investigated: 1) to establish prospective Construct Validity by comparing the PUN-Q to two other parent-report measures (perceived self-efficacy and parenting stress); 2) to examine test-retest reliability of the PUN-Q by comparing two pre-assessment time points; 3) to examine whether the PUN-Q is sensitive measuring potential pre-and-post assessment changes to parental understanding; 4) to explore the relationship between the PUN-Q and child emotional, behavioural and social communication difficulties. These objectives were addressed using data collected from 37 parents, due to time constraints the study was underpowered at Times two and three (n=26, n=11, respectively); bootstrapping confidence intervals were therefore estimated for non-parametric data. Evidence was provided for Construct Validity at Time 1, but not at Time 3. Test-retest reliability was suggested for the PUN-Q between two non-intervention time points. Results suggested that the PUN-Q is responsive to changes over time, and that the clinic’s diagnostic assessment is effective in enhancing parental understanding. The PUN-Q was not shown to be related to child-related outcomes. These preliminary results suggest that the PUN-Q is an important measure that can reliably and conveniently measure parental understanding of their child’s neurodisability symptoms. This study suggests a role for parental understanding within a wider model of parenting stress and coping with disability. Further validation is needed to allow dissemination to the wider neurodisability service, and to less complex symptom presentations

Publishing trends of journals with manuscripts in PubMed Central: changes from 2008–2009 to 2015–2016

Objective: The National Institutes of Health (NIH) public access policy mandates that all articles containing NIH-funded research must be deposited into PubMed Central (PMC). The aim of this study was to assess publishing trends of journals that were not selected for the National Library of Medicine (NLM) collection but contain NIH-funded articles submitted to PMC in compliance with the public access policy. In addition, the authors investigated the degree to which NIH-funded research is published in journals that NLM does not collect due to concerns with the publishers. Methods: We analyzed bibliographic data from the NIH Manuscript Submission system for journals that were not selected for the NLM collection from August 2015 to August 2016. Publications (n=738) were analyzed by language, publishing country, publishing format, and subject, and the results were compared to a similar study of 2008–2009 data. In addition, publications were analyzed by whether their publishers are collected by NLM, as determined by transparency and adherence to publishing best practices. Results: Only a few differences were found between the studies. Most notably, while both studies revealed that most journals were not selected for the NLM collection because they were out of scope (i.e., not biomedical), we noted an increase in 2015–2016 in biomedical journals containing NIH-funded articles that were not added to the collection due to concerns with the publishers. Conclusions: While the current number of NIH-funded manuscripts being published by publishers that are not collected by NLM remains quite small, we noted a substantial increase between 2008–2009 and 2015–2016

Neuropsychosocial profiles of current and future adolescent alcohol misusers

A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models1 can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers2 in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence3. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms4. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention

The handbook for standardized field and laboratory measurements in terrestrial climate change experiments and observational studies (ClimEx)

1. Climate change is a world‐wide threat to biodiversity and ecosystem structure, functioning and services. To understand the underlying drivers and mechanisms, and to predict the consequences for nature and people, we urgently need better understanding of the direction and magnitude of climate change impacts across the soil–plant–atmosphere continuum. An increasing number of climate change studies are creating new opportunities for meaningful and high‐quality generalizations and improved process understanding. However, significant challenges exist related to data availability and/or compatibility across studies, compromising opportunities for data re‐use, synthesis and upscaling. Many of these challenges relate to a lack of an established ‘best practice’ for measuring key impacts and responses. This restrains our current understanding of complex processes and mechanisms in terrestrial ecosystems related to climate change. 2. To overcome these challenges, we collected best‐practice methods emerging from major ecological research networks and experiments, as synthesized by 115 experts from across a wide range of scientific disciplines. Our handbook contains guidance on the selection of response variables for different purposes, protocols for standardized measurements of 66 such response variables and advice on data management. Specifically, we recommend a minimum subset of variables that should be collected in all climate change studies to allow data re‐use and synthesis, and give guidance on additional variables critical for different types of synthesis and upscaling. The goal of this community effort is to facilitate awareness of the importance and broader application of standardized methods to promote data re‐use, availability, compatibility and transparency. We envision improved research practices that will increase returns on investments in individual research projects, facilitate second‐order research outputs and create opportunities for collaboration across scientific communities. Ultimately, this should significantly improve the quality and impact of the science, which is required to fulfil society's needs in a changing world

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

How Popular Musicians Learn : A Way Ahead For Music Education

Table providing a description of the parameters used in gait assessment. (TIFF 753 kb