Perspectives on physical restraint in psychiatric hospital: A qualitative study of mentally ill patients.

The use of physical restraint had caused a series of unexpected impacts on patients, particularly psychological trauma. This qualitative study aimed to identify perspectives on physical restraint among patients with mental health conditions and to seek effective interventions targeting the psychological trauma which is caused by physical restraint. A semi-structured interview was conducted in a public psychiatric hospital in China to explore perspectives on physical restraint among 26 patients who had undergone or witnessed physical restraint. The interview was conducted by experienced and qualified interviewers with mental health service backgrounds. The interviews were recorded and transcribed into words, and then preliminary themes were extracted and coded, finally thematic analysis was used to identify focused themes. Five themes were extracted: these were as follows: (1) The negative effects of physical restraint on patients; (2) The impairment of the relationship between nurse and patient due to physical restraint; (3) The decrease in patients' treatment adherence caused by physical restraint; (4) The positive outcomes of physical restraint; (5) The expectations of patients for improving the quality of nursing care. Conclusively, the use of physical restraint had critical impacts on patients, including psychological trauma, destruction of the nurse-patient relationship, and decreased adherence of treatment. These negative effects could impede clinical work.

An Anti-UAV Long-Term Tracking Method with Hybrid Attention Mechanism and Hierarchical Discriminator.

To prevent unmanned aerial vehicles (UAVs) from threatening public security, anti-UAV object tracking has become a critical issue in industrial and military applications. However, tracking UAV objects stably is still a challenging issue because the scenarios are complicated and the targets are generally small. In this article, a novel long-term tracking architecture composed of a Siamese network and re-detection (SiamAD) is proposed to efficiently locate UAV targets in diverse surroundings. Specifically, a new hybrid attention mechanism module is exploited to conduct more discriminative feature representation and is incorporated into a Siamese network. At the same time, the attention-based Siamese network fuses multilevel features for accurately tracking the target. We further introduce a hierarchical discriminator for checking the reliability of targeting, and a discriminator-based redetection network is utilized for correcting tracking failures. To effectively catch up with the appearance changes of UAVs, a template updating strategy is developed in long-term tracking tasks. Our model surpasses many state-of-the-art models on the anti-UAV benchmark. In particular, the proposed method can achieve 13.7% and 16.5% improvements in success rate and precision rate, respectively, compared with the strong baseline SiamRPN++.

WDA: An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method.

With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from equipment. However, in the actual situation, it is costly and time-consuming to close down machines and inspect components. This seriously impedes the practical application of data-driven diagnosis. In comparison, the full-labeled machine signals from test rigs or online datasets can be achieved easily, which is helpful for the diagnosis of real equipment. Thus, we introduced an improved Wasserstein distance-based transfer learning method (WDA), which learns transferable features between labeled and unlabeled signals from different forms of equipment. In WDA, Wasserstein distance with cosine similarity is applied to narrow the gap between signals collected from different machines. Meanwhile, we use the Kuhn-Munkres algorithm to calculate the Wasserstein distance. In order to further verify the proposed method, we developed a set of case studies, including two different mechanical parts, five transfer scenarios, and eight transfer learning fault diagnosis experiments. WDA reached an average accuracy of 93.72% in bearing fault diagnosis and 84.84% in ball screw fault diagnosis, which greatly surpasses state-of-the-art transfer learning fault diagnosis methods. In addition, comprehensive analysis and feature visualization are also presented.

Effectiveness of CRSCE-Based De-escalation Training on Reducing Physical Restraint in Psychiatric Hospitals: A Cluster Randomized Controlled Trial.

Background: The use of physical restraint (PR) causes clinical and ethical issues; great efforts are being made to reduce the use of PR in psychiatric hospitals globally. Aim: This study aimed to examine the effectiveness of CRSCE-based de-escalation training on reducing PR in psychiatric hospitals. Method: The proposed study adopted cluster randomized controlled trial design. Twelve wards of a psychiatric hospital were randomly allocated to experimental group (n = 6) and control group (n = 6). Wards of control group were assigned to routine training regarding PR; wards of experimental group underwent the same routine training while additionally received CRSCE-based de-escalation training. Before and after CRSCE-based de-escalation training, the frequency of and the duration of PR, and the numbers and level of unexpected events caused by PR, were recorded. Results: After CRSCE-based de-escalation training, the frequency (inpatients and patients admitted within 24 h) of and the duration of PR of experimental group, showed a descending trend and were significantly lower than those of control group (P < 0.01); compared to control group, the numbers of unexpected events (level II and level III) and injury caused by PR of experimental group had been markedly reduced (P < 0.05). Conclusions: CRSCE-based de-escalation training would be useful to reduce the use of PR and the unexpected event caused by PR in psychiatric hospitals. The modules of CRSCE-based de-escalation training can be adopted for future intervention minimizing clinical use of PR. Clinical Trial Registration: This study was registered at Chinese Clinical Trial Registry (Registration Number: ChiCTR1900022211).

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds.

When performing fault diagnosis tasks on bearings, the change of any bearing's rotation speed will cause the frequency spectrum of bearing fault characteristics to be blurred. This makes it difficult to extract stable fault features based on manual or intelligent methods, resulting in a decrease in diagnostic accuracy. In this paper, a two-stage, intelligent fault diagnosis method (order-tracking one-dimensional convolutional neural network, OT-1DCNN) is proposed to deal with the problem of fault diagnosis under variable speed conditions. Firstly, the order tracking algorithm is used to resample the monitoring data obtained under different rotation speeds. Then, the one-dimensional convolutional neural network is adopted to extract features of the fault data. Finally, the fault type of collected data can be obtained by fully connected networks based on the features extracted. In the time domain, while the proposed algorithm only relies on the fault data collected under one speed as the training dataset, it is capable of doing fault diagnosis under different speed conditions. In the condition with the largest difference in speed with each dataset, the accuracy of the proposed method is higher than the baseline methods by 0.54% and 11.00%-on CWRU dataset and our own dataset respectively. The results show that the proposed method performs well in dealing with the fault diagnosis under the condition of variable speeds.

Evaluating the effectiveness of a CRSCE-based de-escalation training program among psychiatric nurses: a study protocol for a cluster randomized controlled trial.

BACKGROUND: The high incidence of workplace violence (WPV) in clinical mental health settings has caused a series of negative impacts on nurses, which has subsequently increased public concern. De-escalation (DE) is recommended as a training program which aims at providing nurses with skills and strategies to more effectively respond and manage WPV. Very few studies have examined the effectiveness of DE training, with current studies possessing various limitations due to their design and small sample sizes. By using a cluster randomized controlled design, the proposed study aims to evaluate the effectiveness of a CRCSE-based DE training programs among psychiatric nurses. METHOD: A cluster randomized controlled trial, with a 6-month follow-up period after the end of the intervention, will be conducted among psychiatric hospitals in Guangdong, China. The randomization unit is each involved psychiatric hospital. Participants in the control group will be assigned to routine WPV management training, participants of the intervention group will undergo the same training while additionally receiving DE training. The DE training will include the following five modules: communication, response, solution, care, and environment (CRSCE). Primary outcomes are objective clinical indicators, which will be extracted from the information systems of the enrolled hospitals. These include the incidence of WPV, injuries caused by WPV, and the use of coercion (physical restraint and seclusion) by nurses. Secondary outcomes, aims at evaluating the effects of DE training on nurses, include the capacity of DE, DE confidence, level of job burnout, and professional quality of life. Data will be collected at baseline (T0), at 3 months (T1, intervention completed), and at 6 months after intervention (T2, follow-up). DISCUSSION: This study will offer trial-based evidence of the efficacy of a DE training program targeted at WPV among psychiatric nurses. DE training is expected to reduce both the total incidence and negative impacts of WPV, with additional improvements in psychiatric nurses' coping skills. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR1900022211 . Prospectively registered on 30 March 2019.

Ethical consideration on use of seclusion in mental health services.

Seclusion was widely used in mental health service, which had caused various negative effects on patients and nurses. In China, the clinical use of seclusion was gradually increasing, which had led to ethical dilemma and had gained public concern. This article aimed to synthesize the ethical issue according to the principle of autonomy, justice, beneficence, and non-maleficence. Given that nursing workforce was limited and work burden among psychiatric nurses was heavy, seclusion was one of coercive interventions managing aggressive behavior. In relation to cope with ethical dilemma, it was proposed to improve therapeutic environment, and to apply de-escalation technique. Additionally, reducing clinical use and adverse effects of seclusion was also important, this goal would be achieved by building appropriate patient-nurse relationship, increasing staff engagement, and promoting guideline of seclusion.