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Employees’ affective responses to their work have increasingly been linked to their subjective well-being and their quality of life (QOL) overall. One source of subjective well-being for individuals is the credit they receive for organizational successes and the blame they receive for failures. This chapter presents two ethical dilemmas involving blame and credit attributions. In determining how to address these dilemmas, we review the research literature on responsibility assignments, integrate recent research on blame contagion, describe how this process can affect employees’ willingness to take risks and report errors, and examines how these processes can impact an organization’s capacity to learn. We finally integrate these findings in a model of blame assignment in organizations, proposing structural and cultural interventions that may help to address some of the problematic outcomes of blame and credit attributions for QWL.

Objectives: Misplaced ECG electrodes can cause changes in ECG recordings, which could have an impact on clinical decisions. We aimed to determine the inter-rater reliability of ECG electrode placement by senior clinical staff in the ED. Methods: A prospective observational study was conducted in adult patients undergoing an ECG as part of their routine ED care. Adhesive electrodes were left in place after an ECG had been performed by the treating nurse, and subsequently each patient was assessed by two of the three investigators. Each investigator independently recorded the location of the chest electrodes relative to the recommended standard positions. Displacement of the electrodes from the standard positions was measured in the vertical and horizontal planes. The age, sex, weight, height and chest circumference was also recorded. Comparisons were made between investigators to determine variability in assessment of the standard positions. Results: Measurement of horizontal and vertical displacement for each of the six chest leads in the 77 patients resulted in 924 paired measurements. There was substantial inter-rater variation in the measurement of both vertical (mean 13.5 mm, range 0–105 mm) and horizontal (mean 16.5 mm, range 0–120 mm) displacement. This variation was greater in the lateral chest leads and was more marked in women than in men, especially in the vertical plane (lead V6: men 14.5 mm vs women 27.0 mm, P

We report select outcomes from an evaluation of Project Wall Talk, a community-based, peer-led HIV prevention education program implemented in 36 Texas State prison units. Peer educators completed questionnaires prior to receipt of a 40-hour intensive training (N = 590) and at 9-month follow-up (N = 257). Students (N = 2506) completed questionnaires pre- and post-receipt of peer educator-led HIV education sessions. Peer educators and their students showed significant increases in HIV-related knowledge. Peer educators showed significant increases in assessment of their skills as educators. For both peer educators and students, significant differences in HIV-related knowledge were indicated across categories of prior educational level attained and race/ethnicity; no such differences were indicated at follow-up. Compared with baseline, a significantly greater proportion of peer educators reported ever having had an HIV test. After receiving peer-led education, a significantly smaller proportion of students reported they knew their HIV status and more indicated plans to take an HIV test. Additionally, in months 12 and 18 following program implementation, the numbers of HIV tests at the five units that implemented the peer education program were roughly twice that of five, matched comparison units without the peer education program. Based on peer educator reports, we projected that peer educators (N = 257) may have as many as 84,000 or more annual opportunities to share HIV-related knowledge with other prisoners outside the classroom.

Motivation: Genotype–phenotype modeling problems are often overcomplete, or ill-posed, since the number of potential predictors—genes, proteins, mutations and their interactions—is large relative to the number of measured outcomes. Such datasets can still be used to train sparse parameter models that generalize accurately, by exerting a principle similar to Occam's Razor: When many possible theories can explain the observations, the most simple is most likely to be correct. We apply this philosophy to modeling the drug response of Type-1 Human Immunodeficiency Virus (HIV-1). Owing to the decreasing expense of genetic sequencing relative to in vitro phenotype testing, a statistical model that reliably predicts viral drug response from genetic data is an important tool in the selection of antiretroviral therapy (ART). The optimization techniques described will have application to many genotype–phenotype modeling problems for the purpose of enhancing clinical decisions. Results: We describe two regression techniques for predicting viral phenotype in response to ART from genetic sequence data. Both techniques employ convex optimization for the continuous subset selection of a sparse set of model parameters. The first technique, the least absolute shrinkage and selection operator, uses the l1 norm loss function to create a sparse linear model; the second, the support vector machine with radial basis kernel functions, uses the ε-insensitive loss function to create a sparse non-linear model. The techniques are applied to predict the response of the HIV-1 virus to 10 reverse transcriptase inhibitor and 7 protease inhibitor drugs. The genetic data are derived from the HIV coding sequences for the reverse transcriptase and protease enzymes. When tested by cross-validation with actual laboratory measurements, these models predict drug response phenotype more accurately than models previously discussed in the literature, and other canonical techniques described here. Key features of the methods that enable this performance are the tendency to generate simple models where many of the parameters are zero, and the convexity of the cost function, which assures that we can find model parameters to globally minimize the cost function for a particular training dataset. Availability: Results, tables and figures are available at Contact: mrabinowitz@genesecurity.net Supplementary information: An Appendix to accompany this article is available at Bioinformatics online.