Framework to Evaluate Level of Good Faith in Implementations of Public Dashboards

To hold governments accountable to open government data (GD) standards, public dashboards need to be evaluated in terms of how well they meet public needs. To assist with that effort, this chapter presents a framework and rubric by which public dashboards can be evaluated for their level of good faith implementation. It starts by reviewing challenges to governments sharing data in good faith despite increasing open government data (OGD) policies and laws being put in place globally. Next, it presents a use-case in which the authors explain how they examined a public dashboard in their local context that appeared to be following OGD, but not in good faith, and developed an alternative implementation that appeared to increase the level of good faith. The framework and rubric proposed were used to successfully compare and contrast the level of good faith of both implementations, as well as another public dashboard described in the scientific literature, and to generate recommendations to increase the level of good faith. In conclusion, the utility of this framework and rubric for evaluating and comparing good faith in public implementations of dashboards was demonstrated, and researchers are encouraged to build upon this research to quantify the level of good faith in public dashboards as a way of increasing oversight of OGD compliance

Patient-Centered Medicine and Prevention of Munchausen Syndrome by Proxy

Munchausen syndrome by proxy (MSbP) is known by many names and is considered the deadliest form of child abuse. Although the condition was named in 1976 and there is now a substantial body of scientific literature about this type of abuse, to date, patient‐centered approaches to early identification, intervention, and prevention have been absent from this literature. The purpose of this chapter is to recommend patient‐centered approaches to identifying MSbP in the clinical setting to facilitate prevention and early intervention. It also recommends patient‐centered practices that can be implemented to reduce the MSbP‐related morbidity and mortality contributed by the healthcare system. The evolving nomenclature and definition of MSbP abuse has been an obstacle to achieving scientific consensus on the topic. Yet, the body of scientific literature on the subject is large. This literature is reviewed to enumerate the healthcare system\u27s contribution to MSbP abuse. The Haddon matrix, a public health framework, is applied to MSbP abuse in order to guide the development of recommendations of patient‐centered approaches that should be implemented to reduce the healthcare system\u27s contribution to the morbidity and mortality that MSbP victims face

Psychometrics of Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) Scores

A sample of 183 medical students completed the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT V2.0). Scores on the test were examined for evidence of reliability and factorial validity. Although Cronbach\u27s alpha for the total scores was adequate (.79), many of the scales had low internal consistency (scale alphas ranged from .34 to .77; median = .48). Previous factor analyses of the MSCEIT are critiqued and the rationale for the current analysis is presented. Both confirmatory and exploratory factor analyses of the MSCEIT item parcels are reported. Pictures and faces items formed separate factors rather than loading on a Perception factor. Emotional Management appeared as a factor, but items from Blends and Facilitation failed to load consistently on any factor, rendering factors for Emotional Understanding and Emotional Facilitation problematic

Comparison of Trait and Ability Measures of Emotional Intelligence in Medical Students

Context Emotional intelligence (EI), the ability to perceive emotions in the self and others, and to understand, regulate and use such information in productive ways, is believed to be important in health care delivery for both recipients and providers of health care. There are two types of EI measure: ability and trait. Ability and trait measures differ in terms of both the definition of constructs and the methods of assessment. Ability measures conceive of EI as a capacity that spans the border between reason and feeling. Items on such a measure include showing a person a picture of a face and asking what emotion the pictured person is feeling; such items are scored by comparing the test‐taker’s response to a keyed emotion. Trait measures include a very large array of non‐cognitive abilities related to success, such as self‐control. Items on such measures ask individuals to rate themselves on such statements as: ‘I generally know what other people are feeling.’ Items are scored by giving higher scores to greater self‐assessments. We compared one of each type of test with the other for evidence of reliability, convergence and overlap with personality. Methods Year 1 and 2 medical students completed the Meyer–Salovey–Caruso Emotional Intelligence Test (MSCEIT, an ability measure), the Wong and Law Emotional Intelligence Scale (WLEIS, a trait measure) and an industry standard personality test (the Neuroticism–Extroversion–Openness [NEO] test). Results The MSCEIT showed problems with reliability. The MSCEIT and the WLEIS did not correlate highly with one another (overall scores correlated at 0.18). The WLEIS was more highly correlated with personality scales than the MSCEIT. Conclusions Different tests that are supposed to measure EI do not measure the same thing. The ability measure was not correlated with personality, but the trait measure was correlated with personality

A Posteriori Modelling-Discretization Error Estimate for Elliptic Problems with L ∞-Coefficients

We consider elliptic problems with complicated, discontinuous diffusion tensor A0. One of the standard approaches to numerically treat such problems is to simplify the coefficient by some approximation, say Aε, and to use standard finite elements. In [19] a combined modelling-discretization strategy has been proposed which estimates the discretization and modelling errors by a posteriori estimates of functional type. This strategy allows to balance these two errors in a problem adapted way. However, the estimate of the modelling error is derived under the assumption that the difference A0 − Aε is bounded in the L∞-norm, which requires that the approximation of the coefficient matches the discontinuities of the original coefficient. Therefore this theory is not appropriate for applications with discontinuous coefficients along complicated, curved interfaces. Based on bounds for A0 − Aε in an L q -norm with q < ∞ we generalize the combined modelling-discretization strategy to a larger class of coefficients.peerReviewe

Successful adjuvant-free vaccination of BALB/c mice with mutated amyloid β peptides-12

G/ml (50 μl/well), plasma were diluted at 1:2048 dilutions. There is no statistically significant reduction four months after vaccination. PWT = Wild type Aβ1–42, PFM = Aβ1–42 with Flemish mutation, PDM = Aβ1–42 with Dutch mutation, PFDM = Aβ1–42 with Flemish and Dutch mutation, P22W = Aβ1–42 with novel mutation at 22, P24M = Aβ1–42 with novel mutation at 24 amino acid.<p><b>Copyright information:</b></p><p>Taken from "Successful adjuvant-free vaccination of BALB/c mice with mutated amyloid β peptides"</p><p>http://www.biomedcentral.com/1471-2202/9/25</p><p>BMC Neuroscience 2008;9():25-25.</p><p>Published online 18 Feb 2008</p><p>PMCID:PMC2270279.</p><p></p

Successful adjuvant-free vaccination of BALB/c mice with mutated amyloid β peptides-5

E antigens at 20 μg/ml (50 μl/well) and plasma diluted at 1:1024 dilutions. Blue bar represents non-adjuvant vaccine (current study), while purple bar represents results from a previous adjuvant study [10]. PWT = Wild type Aβ1–42, PWT+MPL = Wild type Aβ1–42 mixed with monophosphoryl lipid A (MPL) as an adjuvant.<p><b>Copyright information:</b></p><p>Taken from "Successful adjuvant-free vaccination of BALB/c mice with mutated amyloid β peptides"</p><p>http://www.biomedcentral.com/1471-2202/9/25</p><p>BMC Neuroscience 2008;9():25-25.</p><p>Published online 18 Feb 2008</p><p>PMCID:PMC2270279.</p><p></p