Promoting novelty, rigor, and style in energy social science: towards codes of practice for appropriate methods and research design

A series of weaknesses in creativity, research design, and quality of writing continue to handicap energy social science. Many studies ask uninteresting research questions, make only marginal contributions, and lack innovative methods or application to theory. Many studies also have no explicit research design, lack rigor, or suffer from mangled structure and poor quality of writing. To help remedy these shortcomings, this Review offers suggestions for how to construct research questions; thoughtfully engage with concepts; state objectives; and appropriately select research methods. Then, the Review offers suggestions for enhancing theoretical, methodological, and empirical novelty. In terms of rigor, codes of practice are presented across seven method categories: experiments, literature reviews, data collection, data analysis, quantitative energy modeling, qualitative analysis, and case studies. We also recommend that researchers beware of hierarchies of evidence utilized in some disciplines, and that researchers place more emphasis on balance and appropriateness in research design. In terms of style, we offer tips regarding macro and microstructure and analysis, as well as coherent writing. Our hope is that this Review will inspire more interesting, robust, multi-method, comparative, interdisciplinary and impactful research that will accelerate the contribution that energy social science can make to both theory and practice

Disparity in Quality of Infectious Disease vs Addiction Care Among Patients With Injection Drug Use–Associated Staphylococcus aureus Bacteremia

Abstract Evidence-based interventions for Staphylococcus aureus bacteremia (SAB) are well known, but it is unclear how they are implemented among patients with injection drug use–associated (IDU) SAB. Of 46 patients with IDU-SAB identified, all received high-quality SAB management; however, few received appropriate recognition or treatment of their underlying substance use disorder

First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage

AbstractAfter psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peri-traumatic brain activation was able to predict later intrusive memories (i.e. before they had happened). To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma film) via a prospective event-related design was able to capture an individual's later intrusive memories. Results showed widespread increases in brain activation at encoding when viewing a scene in the scanner that would later return as an intrusive memory in the real world. These fMRI results were replicated in a second study. While traditional mass univariate regression analysis highlighted an association between brain processing and symptomatology, this is not the same as prediction. Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory. We also report here brain networks key in intrusive memory prediction. MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative

Objective In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Materials and Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require

Malnutrition and Its Determinants Are Associated with Suboptimal Cognitive, Communication, and Motor Development in Tanzanian Children

© 2015 American Society for Nutrition. Background: A large volume of literature has shown negative associations between stunting and child development; however, there is limited evidence for associations with milder forms of linear growth faltering and determinants of malnutrition in developing countries. Objective: The objective of this study was to assess the association between anthropometric growth indicators across their distribution and determinants of malnutrition with development of Tanzanian children. Methods: We used the Bayley Scales of Infant Development III to assess a cohort of 1036 Tanzanian children between 18 and 36 mo of age who were previously enrolled in a neonatal vitamin A trial. Linear regression models were used to assess standardized mean differences in child development for anthropometry z scores, along with pregnancy, delivery, and early childhood factors. Results: Height-for-age z score (HAZ) was linearly associated with cognitive, communication, and motor development z scores across the observed range in this population (all P values for linear relation \u3c 0.05). Each unit increase in HAZ was associated with +0.09 (95% CI: 0.05, 0.13), +0.10 (95% CI: 0.07, 0.14), and +0.13 (95% CI: 0.09, 0.16) higher cognitive, communication, and motor development z scores, respectively. The relation of weight-for-height z score (WHZ) was nonlinear with only wasted children (WHZ \u3c -2) experiencing deficits (P values for nonlinear relation \u3c 0.05). Wasted children had -0.63 (95% CI: -0.97, -0.29), -0.32 (95% CI: -0.64, 0.01), and -0.54 (95% CI: -0.86, -0.23) z score deficits in cognitive, communication, and motor development z scores, respectively, relative to nonwasted children. Maternal stature and flush toilet use were associated with higher cognitive and motor z scores, whereas being born small for gestational age (SGA) was associated with a -0.16 (95% CI: -0.30, -0.01) z score deficit in cognition. Conclusions: Mild to severe chronic malnutrition was associated with increasing developmental deficits in Tanzanian children, whereas only wasted children exhibited developmental delays during acute malnutrition. Interventions to reduce SGA, improve sanitation, and increase maternal stature may have positive effects on child development. This trial was registered with the Australian New Zealand Clinical Trials Registry as ACTRN12610000636055