Nurses' practices and their influencing factors in palliative care.

Background: In 2017, the Chinese government launched a pilot project in palliative care, in which Shanghai was a pioneer. Nurses play a key role in palliative care services as they are the main providers improving the quality of services for patients and their families. However, little is known about practices and influencing factors in the field of palliative care from a nursing perspective in China. This is an original empirical study that has meticulously analyzed the interrelationship and intensity between practices and other factors among nurses in the initial stage of palliative care in primary healthcare institutions in Shanghai, China. Methods: A descriptive-correlational study design was used to sample 2,829 eligible palliative care nurses by purposive sampling survey in 225 healthcare institutions in Shanghai, China. Descriptive analyses were performed using IBM SPSS 24.0 software. Structural equation modeling was applied to analyze the data by AMOS 20.0. Data were collected using the well-designed Knowledge, Attitudes, and Practices of Hospice Care (KAPHC) scale. Results: The final model showed a good model fit. Self-efficacy directly influenced practices (ß = 0.506, P < 0.01) and indirectly influenced practices (ß = 0.028, P < 0.01) through intention. Subjective norm directly influenced practices (ß = 0.082, P < 0.01) and indirectly influenced practices (ß = 0.030, P < 0.01) through intention. Intention (ß = 0.152, P < 0.01) and knowledge (ß = 0.068, P < 0.01) directly influenced practices. Perceived susceptibility (ß = -0.027, P < 0.01), perceived benefits (ß = -0.017, P < 0.01), and perceived barriers (ß = -0.014, P < 0.01) indirectly influenced practices through intention. Conclusion: This study provided evidence of the associations of knowledge, perceived susceptibility, benefits, barriers, subjective norm, self-efficacy, intention, and practices among nurses concerning palliative care and interventions improving their actual work practices. Our findings revealed that self-efficacy, intention, and subjective norms greatly influenced practices. It is imperative to take interventions that focus precisely on self-efficacy, intention, and subjective norms to improve nurses' practices.

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification.

The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user's trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.