An assessment of residents’ and fellows’ personal finance literacy: An unmet medical education need

Objectives: This study aimed to assess residents' and fellows' knowledge of finance principles that may affect their personal financial health. Methods: A cross-sectional, anonymous, web-based survey was administered to a convenience sample of residents and fellows at two academic medical centers. Respondents answered 20 questions on personal finance and 28 questions about their own financial planning, attitudes, and debt. Questions regarding satisfaction with one's financial condition and investment-risk tolerance used a 10-point Likert scale (1=lowest, 10=highest). Of 2,010 trainees, 422 (21%) responded (median age 30 years; interquartile range, 28-33). Results: The mean quiz score was 52.0% (SD = 19.1). Of 299 (71%) respondents with student loan debt, 144 (48%) owed over $200,000. Many respondents had other debt, including 86 (21$25,000. Respondents' mean satisfaction with their current personal financial condition was 4.8 (SD = 2.5) and investment-risk tolerance was 5.3 (SD = 2.3). Indebted trainees reported lower satisfaction than trainees without debt (4.4 vs. 6.2, F (1,419) = 41.57, p < .001). Knowledge was moderately correlated with investment-risk tolerance (r=0.41, p < .001), and weakly correlated with satisfaction with financial status (r=0.23, p < .001). Conclusions: Residents and fellows had low financial literacy and investment-risk tolerance, high debt, and deficits in their financial preparedness. Adding personal financial education to the medical education curriculum would benefit trainees. Providing education in areas such as budgeting, estate planning, investment strategies, and retirement planning early in training can offer significant long-term benefits.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Predictive models for nanotoxicology: current challenges and future opportunities.

Characterizing the risks posed by nanomaterials is extraordinarily complex because these materials can have a wide range of sizes, shapes, chemical compositions and surface modifications, all of which may affect toxicity. There is an urgent need for a testing strategy that can rapidly and efficiently provide a screening approach for evaluating the potential hazard of nanomaterials and inform the prioritization of additional toxicological testing where necessary. Predictive toxicity models could form an integral component of such an approach by predicting which nanomaterials, as a result of their physico-chemical characteristics, have potentially hazardous properties. Strategies for directing research towards predictive models and the ancillary benefits of such research are presented here

Impacts of the Primary School Free Breakfast Initiative on socio-economic inequalities in breakfast consumption among 9–11-year-old schoolchildren in Wales

Objectives - Universal interventions may widen or narrow inequalities if disproportionately effective among higher or lower socio-economic groups. The present paper examines impacts of the Primary School Free Breakfast Initiative in Wales on inequalities in children's dietary behaviours and cognitive functioning.&lt;p&gt;&lt;/p&gt; Design Cluster - randomised controlled trial. Responses were linked to free school meal (FSM) entitlement via the Secure Anonymised Information Linkage databank. Impacts on inequalities were evaluated using weighted school-level regression models with interaction terms for intervention × whole-school percentage FSM entitlement and intervention × aggregated individual FSM entitlement. Individual-level regression models included interaction terms for intervention × individual FSM entitlement.&lt;p&gt;&lt;/p&gt; Setting - Fifty-five intervention and fifty-six wait-list control primary schools.&lt;p&gt;&lt;/p&gt; Subjects - Approximately 4500 children completed measures of dietary behaviours and cognitive tests at baseline and 12-month follow-up.&lt;p&gt;&lt;/p&gt; Results School-level models indicated that children in intervention schools ate a greater number of healthy items for breakfast than children in control schools (b = 0·25; 95 % CI 0·07, 0·44), with larger increases observed in more deprived schools (interaction term b = 1·76; 95 % CI 0·36, 3·16). An interaction between intervention and household-level deprivation was not significant. Despite no main effects on breakfast skipping, a significant interaction was observed, indicating declines in breakfast skipping in more deprived schools (interaction term b = −0·07; 95 % CI −0·15, −0·00) and households (OR = 0·67; 95 % CI 0·46, 0·98). No significant influence on inequality was observed for the remaining outcomes.&lt;p&gt;&lt;/p&gt; Conclusions - Universal breakfast provision may reduce socio-economic inequalities in consumption of healthy breakfast items and breakfast skipping. There was no evidence of intervention-generated inequalities in any outcomes

Towards an art and science of decision aiding for water management and planning: a participatory modelling process

Planning and management of water resources are faced with increasingly high levels of complexity, uncertainty and conflict. Traditional technical and top-down management strategies have proved inadequate, forcing a move to more integrated forms of management, planning and decision making that can include stakeholders and communities, as well as technical experts and policy makers. These integrated forms of management require not only good technical or scientific ability, but a range of art-like skills including communication, creativity and the capacity to acknowledge and integrate diverse points of view. However, processes designed to aid such inter-organisational or multi-stakeholder decisionmaking are rare and in need of investigation. This paper proposes a process of participatory modelling using a series of semi-structured collective decision cycles which can aid decisions involving multiple stakeholders in water management and planning. The participatory modelling process outlined in this paper is designed to capture and integrate both tacit and explicit knowledge from stakeholders, right from the problem identification phase through to the final decision making, implementation and ongoing monitoring and evaluation. A brief idealised example of the participatory modelling process testing in Montpellier, France, is highlighted, as well as further questions and identified priority research areas

Towards an art and science of decision aiding for water management and planning: a participatory modelling process

Planning and management of water resources are faced with increasingly high levels of complexity, uncertainty and conflict. Traditional technical and top-down management strategies have proved inadequate, forcing a move to more integrated forms of management, planning and decision making that can include stakeholders and communities, as well as technical experts and policy makers. These integrated forms of management require not only good technical or scientific ability, but a range of art-like skills including communication, creativity and the capacity to acknowledge and integrate diverse points of view. However, processes designed to aid such inter-organisational or multi-stakeholder decisionmaking are rare and in need of investigation. This paper proposes a process of participatory modelling using a series of semi-structured collective decision cycles which can aid decisions involving multiple stakeholders in water management and planning. The participatory modelling process outlined in this paper is designed to capture and integrate both tacit and explicit knowledge from stakeholders, right from the problem identification phase through to the final decision making, implementation and ongoing monitoring and evaluation. A brief idealised example of the participatory modelling process testing in Montpellier, France, is highlighted, as well as further questions and identified priority research areas

Implications of the relationships between intelligence and physical fitness

The primary purpose of this paper is to survey the literature with regard to tests concerned with the relationships between intelligence and physical fitness. The writer felt that, a general background relative to the scope of the problem would be beneficial to her understanding. Far this reason, the paper includes a brief historical background on the concept of mens saga in corpore sano and a general discussion of psychological and physical fitness testing. The paper is expanded to delve superficially into the physiological basis of intelligence, mental and physical growth correlations, and superior and inferior intellectual deviates. These three areas seem to have strong implications for education

Evaluation of a weighting approach for performing sensitivity analysis after multiple imputation.

BACKGROUND: Multiple imputation (MI) is a well-recognised statistical technique for handling missing data. As usually implemented in standard statistical software, MI assumes that data are 'Missing at random' (MAR); an assumption that in many settings is implausible. It is not possible to distinguish whether data are MAR or 'Missing not at random' (MNAR) using the observed data, so it is desirable to discover the impact of departures from the MAR assumption on the MI results by conducting sensitivity analyses. A weighting approach based on a selection model has been proposed for performing MNAR analyses to assess the robustness of results obtained under standard MI to departures from MAR. METHODS: In this article, we use simulation to evaluate the weighting approach as a method for exploring possible departures from MAR, with missingness in a single variable, where the parameters of interest are the marginal mean (and probability) of a partially observed outcome variable and a measure of association between the outcome and a fully observed exposure. The simulation studies compare the weighting-based MNAR estimates for various numbers of imputations in small and large samples, for moderate to large magnitudes of departure from MAR, where the degree of departure from MAR was assumed known. Further, we evaluated a proposed graphical method, which uses the dataset with missing data, for obtaining a plausible range of values for the parameter that quantifies the magnitude of departure from MAR. RESULTS: Our simulation studies confirm that the weighting approach outperformed the MAR approach, but it still suffered from bias. In particular, our findings demonstrate that the weighting approach provides biased parameter estimates, even when a large number of imputations is performed. In the examples presented, the graphical approach for selecting a range of values for the possible departures from MAR did not capture the true parameter value of departure used in generating the data. CONCLUSIONS: Overall, the weighting approach is not recommended for sensitivity analyses following MI, and further research is required to develop more appropriate methods to perform such sensitivity analyses

Anchor geotechnics for floating offshore wind: Current technologies and future innovations

A rapid expansion of the anchor market is required to meet the increasing demand for floating offshore wind. This paper, which is aimed at a broad readership within and beyond geotechnical engineering, summarises the current state-of-the-art and discusses future developments of anchor types and geotechnical design methods.Current anchor technologies are presented via comparative analytical assessments of performance across a range of practical scales and seabed conditions. This analysis demonstrates the relative merits and performance of different anchor types, using simplified cost-performance indicators for each anchor technology. An example outcome is the large differences in anchor efficiency (capacity per unit weight), that are linked to the different ways anchors achieve their holding capacity.Potential improvements in the performance-cost response for each anchor type, through future enhancements, are then explored. These enhancements are categorised as (1) unlocking higher anchor performance through improved design methods with a better understanding of the geotechnical response, (2) upscaling or (3) commoditising of the anchor type, by making larger versions or enabling more efficient mass production and installation, or (4) invention of new anchor technologies. Finally, findings of the different sections are summarised within a single table to enable a quick selection of anchoring solutions

Variable responses of human and non-human primate gut microbiomes to a Western diet

BACKGROUND: The human gut microbiota interacts closely with human diet and physiology. To better understand the mechanisms behind this relationship, gut microbiome research relies on complementing human studies with manipulations of animal models, including non-human primates. However, due to unique aspects of human diet and physiology, it is likely that host-gut microbe interactions operate differently in humans and non-human primates. RESULTS: Here, we show that the human microbiome reacts differently to a high-protein, high-fat Western diet than that of a model primate, the African green monkey, or vervet (Chlorocebus aethiops sabaeus). Specifically, humans exhibit increased relative abundance of Firmicutes and reduced relative abundance of Prevotella on a Western diet while vervets show the opposite pattern. Predictive metagenomics demonstrate an increased relative abundance of genes associated with carbohydrate metabolism in the microbiome of only humans consuming a Western diet. CONCLUSIONS: These results suggest that the human gut microbiota has unique properties that are a result of changes in human diet and physiology across evolution or that may have contributed to the evolution of human physiology. Therefore, the role of animal models for understanding the relationship between the human gut microbiota and host metabolism must be re-focused.P40 OD010965 - NIH HHS; P40 RR019963 - NCRR NIH HHS; P51 OD011132 - NIH HHS; R01 RR016300 - NCRR NIH HHS; 5R01RR016300 - NCRR NIH HH