The Brief Symptom Inventory-18 (BSI-18) is widely used to assess psychiatric distress but has not been verified in the Chinese population. From March to April 2019, 293 hospitalized cancer patients, aged 20–87, completed the cross-sectional survey with demographics questionnaire, BSI-18, and PHQ-9. We analyzed the single suicide-related item of PHQ-9 with the full score clinical outpoint for BSI-18 and PHQ-9 using SPSS 22.0 and R 2.15, including Pearson's χ2 test and ROC curve analyses. A Pearson's χ2 test was carried out to compare the three different methods with the gold screening criteria. The p-value was correspondingly to .006, .066, .838. When the PHQ-9?≥?10 criteria for the BSI-18, receiver operating characteristic analysis revealed that AUC values were 0.839, optimal cut-off points for both BSI-18?≥?50, the sensitivity of 85.8%, and 62.5%, respectively. The BSI-18 is suitable for a screening tool for psychological distress and could also be used in clinical settings for preliminary screening of hospitalized cancer patients.
Axiomathes - The non-alethic systems N1 of da Costa and A of Grana are both paraconsistent and paracomplete. Based on them, a multi-agent doxastic logic NADK can be obtained by logical expansion.... 相似文献
In the present article, we demonstrates the use of SAS PROC CALIS to fit various types of Level-1 error covariance structures of latent growth models (LGM). Advantages of the SEM approach, on which PROC CALIS is based, include the capabilities of modeling the change over time for latent constructs, measured by multiple indicators; embedding LGM into a larger latent variable model; incorporating measurement models for latent predictors; and better assessing model fit and the flexibility in specifying error covariance structures. The strength of PROC CALIS is always accompanied with technical coding work, which needs to be specifically addressed. We provide a tutorial on the SAS syntax for modeling the growth of a manifest variable and the growth of a latent construct, focusing the documentation on the specification of Level-1 error covariance structures. Illustrations are conducted with the data generated from two given latent growth models. The coding provided is helpful when the growth model has been well determined and the Level-1 error covariance structure is to be identified. 相似文献