Comparing Patients´ Emotional Experience In Icu with General Wards: A Case Control Study

Background Comparing emotional experiences between patients in ICU and general wards, exploring aspects of patients' relationships with healthcare staff using the Patient Evaluation of Emotional Care during Hospitalisation (PEECH) questionnaire. Methods A project to humanise the ICU had previously been undertaken, heeding the recommendations set out in the Humanisation in Intensive Care Units Best Practices. Based on a preliminary study, an alpha risk of 0.05 and a beta risk of 0.20 was obtained in a two-tailed test. 252 general patients and 252 ICU patients needed to detect a difference equal to or greater than 0.2 units on the PEECH scale. A common standard deviation of 0.8 units was used. 513 questionnaires were collected, 253 from ICU and 260 from patients on general wards. Results Significantly higher scores were achieved by the ICU on sub-scales level of security 2.83 in ICU v. 2.62 on general wards (p < 0.001); level of personal value 2.79 v. 2.57 (p < 0.001); level of knowing 2.64 v. 2.55 (p = 0.035). Not significative differences were found on sub-scale level of connection with mean score of 1.66 v. 1.46 (p = 0.033). Conclusion Significant differences were found on all sub-scales, with the ICU scoring higher than the general wards. On the contrary, no shortcomings were identified for level of security, level of knowing in the care process or level of personal value. The level of connection with staff was not perceived in terms of continuous and coordinated care. Efforts should be made for patients to know the staff caring for them, especially in short stays.

Statistical Analysis Based on Lake Michigan Fish Acoustic Data Using LASSO Method

LASSO method is one of the most popular and more extensive regressions. It has been applied to many fields. However, it is rare seen to research with complicated big data in biology. This paper is to apply LASSO method to Lake Michigan Fish acoustic data. The main techniques include: Elastic Net selection, which tests validation from the average square error (ASE) to predict the error for the model by computing separately for each of these subsets; defaulting group LASSO to test multiple parameters by splitting a couple constituent parameters, such as successive intervals, multiple continuous depth layers, to estimate the Schwarz Bayesian information criterion (SBC) to find the lowest value for the model; The adaptive LASSO selection, which is applied to each of the parameters in constructing the LASSO constraint for weights, that is, the response y has mean zero and the regressor x are scaled to have mean zero and common standard deviation. The empirical results show that the fish density (Y) has strong relationships with area backscattering coefficient (PRC_ABC), secondly, significant interactions with PRC_ABC and Exclude below line depth mean), among PRC_ABC, fish density in the intervals and layers of acoustic survey transect of Lake Michigan.