Establishment of in-hospital nutrition support program for middle-aged and elderly patients with acute decompendated heart failure.

OBJECTIVE: To construct a nutrition support program for middle-aged and elderly patients with acute decompensated heart failure (ADHF) during hospitalization. METHODS: Based on the JBI Evidence-Based Health Care Model as the theoretical framework, the best evidence was extracted through literature analysis and a preliminary nutrition support plan for middle-aged and elderly ADHF patients during hospitalization was formed. Two rounds of expert opinion consultation were conducted using the Delphi method. The indicators were modified, supplemented and reduced according to the expert's scoring and feedback, and the expert scoring was calculated. RESULTS: The response rates of the experts in the two rounds of consultation were 86.7% and 100%, respectively, and the coefficient of variation (CV) for each round was between 0.00% and 29.67% (all < 0.25). In the first round of expert consultation, 4 items were modified, 3 items were deleted, and 3 items were added. In the second round of the expert consultation, one item was deleted and one item was modified. Through two rounds of expert consultation, expert consensus was reached and a nutrition support plan for ADHF patients was finally formed, including 4 first-level indicators, 7 s-level indicators, and 24 third-level indicators. CONCLUSION: The nutrition support program constructed in this study for middle-aged and elderly ADHF patients during hospitalization is authoritative, scientific and practical, and provides a theoretical basis for clinical development of nutrition support program for middle-aged and elderly ADHF patients during hospitalization.

Perspectives and experiences of newly registered nurses surrounding the objective structured clinical examination in an obstetrics and gynecology hospital: A qualitative study in Shanghai, China.

OBJECTIVES: Newly registered nurses in China are required to attend two years of standardized training programs after graduation, and an evaluation of the training program's effectiveness is critical. The objective structured clinical examination is a relatively new and objective approach to exploring the effectiveness of training programs and is increasingly being encouraged and used in clinics. However, the perspectives and experiences of newly registered nurses in obstetrics and gynecology regarding the objective structured clinical examination are unclear. Therefore, the objective of this study was to investigate newly registered nurses' perspectives and experiences of the objective structured clinical examination in an obstetrics and gynecology hospital. DESIGN: This qualitative study was conducted using a phenomenological approach. DATA SOURCES: Twenty-four newly registered nurses taking the objective structured clinical examination in a third-level obstetrics and gynecology hospital in Shanghai, China. REVIEW METHODS: Semi-structured face-to-face interviews were conducted between July and August 2021. The Colaizzi seven-step framework was applied for data analysis. RESULTS: Three main themes and six sub-themes emerged: 1) high satisfaction with the objective structured clinical examination; 2) gaining experience and growing as nurses; and 3) high pressure. CONCLUSION: The objective clinical structured examination can be used to assess the competence of newly registered nurses after training in an obstetrics and gynecology hospital. The examination not only enables an objective and comprehensive evaluation of others and self-evaluation but also leads to positive psychological experiences in newly registered nurses. However, interventions are needed to relieve examination pressure and to provide effective support for participants. The objective clinical structured examination can be incorporated into the training assessment system; this study provides a basis for improving training programs and the training of newly registered nurses.

Training load of newly recruited nurses in Grade-A Tertiary Hospitals in Shanghai, China: a qualitative study.

BACKGROUND: This study aimed to provide insight into the training load of newly recruited nurses in grade-A tertiary hospitals in Shanghai, China. The lack of nurses in hospitals across China has resulted in newly recruited nurses in grade-A tertiary hospitals in Shanghai having to integrate into the work environment and meet the needs of the job quickly; thus, they undergo several training programs. However, an increase in the number of training programs increases the training load of these nurses, impacting the effectiveness of training. The extent of the training load that newly recruited nurses have to bear in grade-A tertiary hospitals in China remains unknown. METHODS: This qualitative study was conducted across three hospitals in Shanghai, including one general hospital and two specialized hospitals, in 2020. There were 15 newly recruited nurses who were invited to participate in semi-structured in-depth interviews with the purpose sampling method. A thematic analysis approach was used to analyze the data. The COREQ checklist was used to assess the overall study. RESULTS: Three themes emerged: external cognitive overload, internal cognitive overload, and physical and mental overload. CONCLUSION: Through qualitative interviews, this study found that the training of newly recruited nurses in Shanghai's grade-A tertiary hospitals is in a state of overload, which mainly includes external cognitive overload, internal cognitive overload, physical and mental overload, as reflected in the form of training overload, the time and frequency of training overload, the content capacity of training overload, the content difficulty of training overload, physiological load overload, and psychological load overload. The intensity and form of the training need to be reasonably adjusted. Newly recruited nurses need to not only improve their internal self-ability, but also learn to reduce internal and external load. Simultaneously, an external social support system needs to be established to alleviate their training burden and prevent burnout.

Analysis of clinical application effects and challenges of early warning system for the high-risk obstetric women of China: A scoping review.

BACKGROUND: Obstetric critical illness is an important factor that leads to an increase in maternal mortality. Early warning assessment can effectively reduce maternal and neonatal mortality and morbidity. However, there are multiple early warning systems, and the effect and applicability of each system in China still need to be explored. OBJECTIVES: To elaborate on the application, effectiveness and challenges of the existing early warning systems for high-risk obstetric women in China and to provide a reference for clinical practice. DESIGN: A scoping review guided by the Arksey and O'Malley framework and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for scoping review (PRISMA-ScR) guidelines. ELIGIBILITY CRITERIA: We included original studies related to early warning and excluded those that were guidelines, consensus and reviews. The included studies were published in Chinese or English by Chinese scholars as of June 2021. DATA SOURCES: CNKI, Wanfang, VIP, Cochrane, CINAHL, Embase, PubMed and Web of Science databases were searched systematically, and the reference sections of the included papers were snowballed. RESULTS: In total, 598 articles were identified. These articles were further refined using keyword searches and exclusion criteria, and 17 articles met the inclusion criteria. We extracted data related to each study's population, methods and results. Early warning tools, outcome indices, effects and challenges are discussed. CONCLUSIONS: Although all studies have shown that early warning systems have good application effects, the use of early warning systems in China is still limited, with poor regional management and poor sensitivity for specific obstetric women. Future research needs to develop more targeted early warning tools for high-risk obstetric women and address the current challenges in clinical applications.

Application of machine learning in predicting the risk of postpartum depression: A systematic review.

BACKGROUND: Postpartum depression (PPD) presents a serious health problem among women and their families. Machine learning (ML) is a rapidly advancing field with increasing utility in predicting PPD risk. We aimed to synthesize and evaluate the quality of studies on application of ML techniques in predicting PPD risk. METHODS: We conducted a systematic search of eight databases, identifying English and Chinese studies on ML techniques for predicting PPD risk and ML techniques with performance metrics. Quality of the studies involved was evaluated using the Prediction Model Risk of Bias Assessment Tool. RESULTS: Seventeen studies involving 62 prediction models were included. Supervised learning was the main ML technique employed and the common ML models were support vector machine, random forest and logistic regression. Five studies (30 %) reported both internal and external validation. Two studies involved model translation, but none were tested clinically. All studies showed a high risk of bias, and more than half showed high application risk. LIMITATIONS: Including Chinese articles slightly reduced the reproducibility of the review. Model performance was not quantitatively analyzed owing to inconsistent metrics and the absence of methods for correlation meta-analysis. CONCLUSIONS: Researchers have paid more attention to model development than to validation, and few have focused on improvement and innovation. Models for predicting PPD risk continue to emerge. However, few have achieved the acceptable quality standards. Therefore, ML techniques for successfully predicting PPD risk are yet to be deployed in clinical environments.

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study.

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotated objects/faces. We launched the UG2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.