潜在类模型
首都(建筑)
心理学
护理部
医学
计算机科学
地理
机器学习
考古
作者
Mei Teng,Jialin Wang,Man Jin,Zhongqing Yuan,Hong He,Shuping Wang,Ren Qianqian
摘要
To determine the psychological capital level of nurses and explore the latent profiles of nurses regarding their psychological capital scores.The use of individual-centered analysis for the connotation of nurses' psychological capital structure is less studied and still needs to be further explored.By the convenience sampling method, 494 clinical nurses from 7 general hospitals in Sichuan province were selected. The study was conducted from December 2022 to February 2023. Latent profile analysis was used for data analysis. We followed STROBE guidelines in this research.The total mean score of nurses' psychological capital is 5.17 (SD = 0.8). The following four latent profiles were identified: "poor" (4.5%), "medium" (22.9%), "well-off" (41.5%), and "rich" (31.1%). Multiple logistic regression showed that the number of hours worked per day and the number of night shifts per month were negative predictors of psychological capital, and psychological training and job satisfaction were protective factors of psychological capital.Our study found that the four profiles can be distinguished by "poor," "well-off," "medium," and "rich" levels of psychological capital. Among them, more than 70% of the nurses belonged to the well-off and rich profiles, and the number of the poor profile was the lowest.The overall psychological capital of clinical nurses is at a medium-high level. Each profile is influenced by multiple sociodemographic factors (i.e., age, working hours, monthly income, psychological training, and job satisfaction).Administrators should develop enhancement strategies to improve the mental health of nurses based on the characteristics of their psychological capital profiles.
科研通智能强力驱动
Strongly Powered by AbleSci AI