High resolution study of low-lying correlation satellites below 25 eV in xenon probed by pulsed-field-ionization-zero-kinetic-energy photoelectron spectroscopy

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The mechanical response of glassy carbon recovered from high pressure

Glassy carbon (GC) is usually considered the prototypical super-elastic material, which can almost fully recover its shape after compression of several gigapascals (GPa). In this work, nanoindentation is used to study the mechanical response of GC, which was subjected to a range of high pressures using a diamond anvil cell (DAC). We show that GC starts to lose its elasticity after compression to 6 GPa and becomes clearly mechanically anisotropic after being compressed beyond ∼30 GPa. Molecular dynamics (MD) simulations are used to calculate Young’s modulus before and after compression. Through our experimental results and MD simulations, we show that the elasticity of GC is at a minimum around 30 GPa but recovers after compression to higher pressures along the DAC compression axis.The authors would like to acknowledge the Australian Research Council (ARC) for funding under the ARC Discovery Project Scheme (Nos. DP190101438, DP170102087, and DP140102331) and M. V. Swain for useful discussions

Hyperfine resolved spectrum of the molecular dication DCl

We have obtained hyperfine-resolved infrared spectra of a PQ23(N) branch line in the v = 2-1 band of the X 3Σ- state of the molecular dication D35Cl2+. Analysis of the hyperfine structure allows us to estimate the magnitude of the Fermi contact interaction for the chlorine nucleus; bF(Cl) = 167 (25) MHz

Internet-based medical education: a realist review of what works, for whom and in what circumstances

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The SIPHER consortium : introducing the new UK hub for systems science in public health and health economic research

The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER (Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium’s vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing

Individual quality of life: adaptive conjoint analysis as an alternative for direct weighting?

In the schedule for the evaluation of individual quality of life (SEIQoL) the weights for five individualized quality of life domains have been derived by judgment analysis and direct weighting (DW). We studied the feasibility and validity of adaptive conjoint analysis (ACA) as an alternative method to derive weights in 27 cancer patients and 20 patients with rheumatoid arthritis. Further, we assessed the convergence between direct weights and weights derived by ACA, and their correlation with global quality-of-life scores. All respondents finished the ACA task, but one in five respondents were upset about the ACA task. Further, the task was vulnerable to judgment ‘errors’, such as inconsistent answers. The agreement between the two weights was low. Both weighted index scores were strongly correlated to the unweighted index score. The relationships between the index score and scores on a visual analogue scale for global individual quality of life and global quality of life were similar whether or not the index score was calculated with DW weights, with ACA weights, or without using weights. We conclude that, because weights did not improve the correlation between the index score and global quality of life scores, it seems sufficient to use the unweighted index score as a measure for global individual quality of life

Is health research undertaken where the burden of disease is greatest? Observational study of geographical inequalities in recruitment to research in England 2013–2018

Background: Research is fundamental to high-quality care, but concerns have been raised about whether health research is conducted in the populations most affected by high disease prevalence. Geographical distribution of research activity is important for many reasons. Recruitment is a major barrier to research delivery, and undertaking recruitment in areas of high prevalence could be more efficient. Regional variability exists in risk factors and outcomes, so research done in healthier populations may not generalise. Much applied health research evaluates interventions, and their impact may vary by context (including geography). Finally, fairness dictates that publically funded research should be accessible to all, so that benefits of participating can be fairly distributed. We explored whether recruitment of patients to health research is aligned with disease prevalence in England. Methods: We measured disease prevalence using the Quality and Outcomes Framework in England (total long-term conditions, mental health and diabetes). We measured research activity using data from the NIHR Clinical Research Network. We presented descriptive data on geographical variation in recruitment rates. We explored associations between the recruitment rate and disease prevalence rate. We calculated the share of patient recruitment that would need to be redistributed to align recruitment with prevalence. We assessed whether associations between recruitment rate and disease prevalence varied between conditions, and over time. Results: There was significant geographical variation in recruitment rates. When areas were ranked by disease prevalence, recruitment was not aligned with prevalence, with disproportionately low recruitment in areas with higher prevalence of total long-term and mental health conditions. At the level of 15 local networks, analyses suggested that around 12% of current recruitment activity would need to be redistributed to align with disease prevalence. Overall, alignment showed little change over time, but there was variation in the trends over time in individual conditions. Conclusions: Geographical variations in recruitment do not reflect the suitability of the population for research. Indicators should be developed to assess the fit between research and need, and to allow assessment of interventions among funders, researchers and patients to encourage closer alignment between research activity and burden

The use of Goal Attainment Scaling in a community health promotion initiative with seniors

<p>Abstract</p> <p>Background</p> <p>Evaluating collaborative community health promotion initiatives presents unique challenges, including engaging community members and other stakeholders in the evaluation process, and measuring the attainment of goals at the collective community level. Goal Attainment Scaling (GAS) is a versatile, under-utilized evaluation tool adaptable to a wide range of situations. GAS actively involves all partners in the evaluation process and has many benefits when used in community health settings.</p> <p>Methods</p> <p>The purpose of this paper is to describe the use of GAS as a potential means of measuring progress and outcomes in community health promotion and community development projects. GAS methodology was used in a local community of seniors (n = 2500; mean age = 76 ± 8.06 SD; 77% female, 23% male) to a) collaboratively set health promotion and community partnership goals and b) objectively measure the degree of achievement, over- or under-achievement of the established health promotion goals. Goal attainment was measured in a variety of areas including operationalizing a health promotion centre in a local mall, developing a sustainable mechanism for recruiting and training volunteers to operate the health promotion centre, and developing and implementing community health education programs. Goal attainment was evaluated at 3 monthly intervals for one year, then re-evaluated again at year 2.</p> <p>Results</p> <p>GAS was found to be a feasible and responsive method of measuring community health promotion and community development progress. All project goals were achieved at one year or sooner. The overall GAS score for the total health promotion project increased from 16.02 at baseline (sum of scale scores = -30, average scale score = -2) to 54.53 at one year (sum of scale scores = +4, average scale score = +0.27) showing project goals were achieved above the expected level. With GAS methodology an amalgamated score of 50 represents the achievement of goals at the expected level.</p> <p>Conclusion</p> <p>GAS provides a "participatory", flexible evaluation approach that involves community members, research partners and other stakeholders in the evaluation process. GAS was found to be "user-friendly" and readily understandable by seniors and other community partners not familiar with program evaluation.</p

Guidance on the Use of Complex Systems Models for Economic Evaluations of Public Health Interventions

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making