Beliefs and Information Seeking in Patients With Cancer in Southwest China: Survey Study (Preprint)

Abstract

BACKGROUND Although previous studies have reported the cancer information seeking behaviors among developed countries, cancer information seeking practice of patients living in low- and middle-income areas is less known. OBJECTIVE This study investigates the beliefs and information seeking patterns of cancer patients in Southwest China. METHODS A questionnaire was designed and data was collected in two hospitals (N=285) in Southwest China. Statistical analyses included bivariate analyses and regressions. RESULTS Patients’ attitudes towards cancer fatalism were significantly influenced by marital status, education and household income. Moreover, endorsing fatalistic belief was positively associated with age (r=0.348, P<0.001). The regression model shows that younger patients (OR=0.959, CI [0.932, 0.987]) and those with higher education (OR=1.749, CI [1.089, 2.810]) are more likely to look for information. Additionally, patients who were less confident in getting information were more likely to find information (OR=1.700, CI [1.145, 2.524]), while fatalism belief was not significant in the regression. CONCLUSIONS This study explored the information seeking patterns of cancer patients in Southwest China. It is found that many Chinese people endorsed cancer fatalism. These pessimistic beliefs about the potential to prevent and to cure cancer exert correlation rather than causality with cancer-related information seeking. However, self-efficacy about the confidence in finding needed cancer information was a significant predictor of seeking out information. CLINICALTRIAL

A neural-network-defined Gaussian mixture model for particle identification applied to the LHCb fixed-target programme

“…being n h a set of calibration candidates for the particle species h. The weights w i can be introduced [7,8] in case a channel is affected by a large background contamination, which has to be statistically subtracted, e.g. with the sPlot technique [9].…”