Long-acting reversible contraception use among residents in obstetrics/gynecology training programs

Background: The objective of the study was to estimate the personal usage of long-acting reversible contraception (LARC) among obstetrics and gynecology (Ob/Gyn) residents in the United States and compare usage between programs with and without a Ryan Residency Training Program (Ryan Program), an educational program implemented to enhance resident training in family planning. Materials and methods: We performed a web-based, cross-sectional survey to explore contraceptive use among Ob/Gyn residents between November and December 2014. Thirty-two Ob/Gyn programs were invited to participate, and 24 programs (75%) agreed to participate. We divided respondents into two groups based on whether or not their program had a Ryan Program. We excluded male residents without a current female partner as well as residents who were currently pregnant or trying to conceive. We evaluated predictors of LARC use using bivariate analysis and multivariable Poisson regression. Results: Of the 638 residents surveyed, 384 (60.2%) responded to our survey and 351 were eligible for analysis. Of those analyzed, 49.3% (95% confidence interval [CI]: 44.1%, 54.5%) reported current LARC use: 70.0% of residents in Ryan Programs compared to 26.8% in non-Ryan Programs (RRadj 2.14, 95% CI 1.63-2.80). Residents reporting a religious affiliation were less likely to use LARC than those who described themselves as non-religious (RRadj 0.76, 95% CI 0.64-0.92). Of residents reporting LARC use, 91% were using the levonorgestrel intrauterine device. Conclusion: LARC use in this population of women's health specialists is substantially higher than in the general population (49% vs. 12%). Ob/Gyn residents in programs affiliated with the Ryan Program were more likely to use LARC

Analysis of the Fabrication Conditions in Organic Field-Effect Transistors

Polymer-based organic field-effect transistors have raised substantial awareness because they enable low-cost, solution processing techniques, and have the potential to be implemented in flexible, disposable organic electronic devices. The performance of these devices is highly dependent on the processing conditions, as well as the intrinsic properties of the polymer. Processing conditions play an important role in semiconductor film formation and device performance. These factors may provide an important link between structure and performance. In this study, an empirical analysis tool, Process Scout, was applied to assess processing factors such as polymer concentration and silicon modification. This sanctioned the creation of a realistic optimization model because common variance was not assumed and the mobility was capably analyzed in the real space. After the analysis of the processing conditions, it was evident that further study on the effect of humidity on performance must be conducted to account for the variance between similarly fabricated devices. The developed process may be applied to expand the study of other organic semiconductors. This process is the first step in creating a standard fabrication protocol, allowing organic field-effect transistors to prosper

SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models

A common limitation of diagnostic tests for detecting social biases in NLP models is that they may only detect stereotypic associations that are pre-specified by the designer of the test. Since enumerating all possible problematic associations is infeasible, it is likely these tests fail to detect biases that are present in a model but not pre-specified by the designer. To address this limitation, we propose SODAPOP (SOcial bias Discovery from Answers about PeOPle) in social commonsense question-answering. Our pipeline generates modified instances from the Social IQa dataset (Sap et al., 2019) by (1) substituting names associated with different demographic groups, and (2) generating many distractor answers from a masked language model. By using a social commonsense model to score the generated distractors, we are able to uncover the model's stereotypic associations between demographic groups and an open set of words. We also test SODAPOP on debiased models and show the limitations of multiple state-of-the-art debiasing algorithms.Comment: EACL 202