Objective. This study’s objective is to establish a nurse-led pain management model for neurointensive care based on the Precede-Proceed model to provide a theoretical basis for clinical pain management in neurointensive care. Methods. ICU nurses were randomly divided into a control group (giving conventional routine pain care) and an experimental group (managed pain based on the Precede-Proceed model). The nurses from the experimental group were trained in the Precede-Proceed-based management. The nurses then treated a total of 410 critically ill patients, and the patients were randomly divided into a control and an intervention group (205 cases/nursing group), and the data were prospectively recorded. Before and after the intervention, the pain assessment ability, discomfort level, satisfaction degree, usage of the analgesic drug, and the incidence of delirium of the patients from the two groups were evaluated. Nurses from both groups also assessed their knowledge of pain, attitude, and pain nursing behaviors using indicated self-designed questionnaires. Results. Before the intervention, there was no statistical difference between the two groups of nurses in their baseline characteristics, pain knowledge, attitude, pain nursing behavior, and pain assessment ability for the patients. After the intervention, the nurses in the experimental group had better pain knowledge, attitude, pain nursing behavior, and pain assessment ability to patients than the nurses in the control group. Patients in the intervention group felt less discomfort, a higher satisfaction degree, reduced use of analgesics, and a lower incidence of delirium than patients in the control group. Conclusion. Pain management based on the Precede-Proceed model was beneficial in improving the care of neurointensive patients.

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In this paper, we introduce a novel Local Gabor Binary Pattern Random Subspace Method (LGBPRSM) for wearing-glasses face recognition. It extracts the discriminating features from facial space based on local-feature method, after that, it constructs multiple classifiers by randomly sampling from the feature set to gain more diversity between classifiers for efficiently recognizing the faces with glasses. Our experimental results on FERET and Yale database prove the advantages of the proposed approach when compared with other methods.

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