The labor experience and satisfaction with childbirth are affected by the care provided (external factors) and individual variables (internal factors). In this paper, we present a descriptive analysis that aims to indicate the strongest correlates of birth experience among a wide range of indicators. The study is a prospective, cross-sectional, self-report survey. It includes the experiences of women giving birth in public and private hospitals in Poland. The two main variables were birth experience and satisfaction with care. The analysis consists of three parts: data pre-processing and initial analysis, explorative investigation, and regression analysis. Among the 15 variables with the highest predictive value regarding birth experience were being informed by the medical personnel, communication, and birth environment. The most significant variables among 15 variables, with the highest predictive value regarding care, were those concerning support, information, and respectful care. The strongest predictor for both, birth experience and satisfaction with care, is the sense of information, with logit coefficients of 0.745 and 1.143, respectively, for birth experience and satisfaction (0.367 and 0.346 for standardized OLS coefficient). The findings demonstrate that by using explanatory variables, one can predict a woman’s description of her satisfaction with perinatal care received in the hospital. On the other hand, they do not have such a significant and robust influence on the birth experience examined by the variables. For both the birth experience and satisfaction with care, the sense of being informed is the highest predictor.

While in recent years a number of new statistical approaches have been proposed to model group differences with a different assumption on the nature of the measurement invariance of the instruments, the tools for detecting local misspecifications of these models have not been fully developed yet. In this study, we present a novel approach using a Deep Neural Network (DNN). We compared the proposed model with the most popular traditional methods: Modification Indices (MI) and Expected Parameter Change (EPC) indicators from the Confirmatory Factor Analysis (CFA) modeling, logistic DIF detection, and sequential procedure introduced with the CFA alignment approach. Simulation studies show that the proposed method outperformed traditional methods in almost all scenarios, or it was at least as accurate as the best one. We also provide an empirical example utilizing European Social Survey data including items known to be miss-translated, which are correctly identified with presented DNN approach.

In this article, an extensive analysis of the performance of the fiber optics-based abrasion sensor that utilizes chirped fiber Bragg grating, is presented. For the response investigation during abrasion, a numerical analysis, based on the transfer matrix method and coupled mode theory, is provided. The influence of the SLED source spectral position in respect to the spectral position of the chirped fiber Bragg grating is evaluated together with the influence of the changes of the ambient temperature of the sensor. Experimental verification of the sensor’s performance is provided, together with the proposition of the packaging of the sensor. In the article, a simple, cost-effective and multiplexation-ready concept of the wear or abrasion sensor system is presented and discussed.