Nurses' compliance to standard precautions prior to and during COVID-19.

AIMS: To measure nurses' compliance with standard precautions during the COVID-19 pandemic, compare findings with previous assessments and describe the barriers affecting nurses' compliance. BACKGROUND: Healthcare providers' compliance with standard precautions is still limited worldwide. Implementation of infection control policies in hospitals is needed internationally, especially during a pandemic. Surprisingly, studies exploring nurses' compliance with standard precautions are lacking during COVID-19. METHODS: A multicenter cross-sectional study was adopted in two Italian hospitals. Nurses' compliance with standard precautions was measured through The Compliance with Standard Precautions Scale (Italian version). An open-ended question explored the barriers to nurses' compliance with standard precautions. Reporting, followed the STROBE guidelines. RESULTS: A total of 201 nurses were enrolled in 2020. Nurses' compliance with standard precautions was suboptimal. A statistically significant improvement in the compliance rate with standard precautions was observed between pre- and during COVID-19 assessments. High compliance was found in the appropriate use of surgical masks, gloves and sharps disposal. Nurses perceived personal, structural and organizational barriers to standard precautions adherence. CONCLUSION: Nurses' compliance with standard precautions was not 100%, and different factors impeded nurses to work safely. Our findings provide institutional leaders and educators with the basis for implementing policies to optimize nurse safety, well-being and patient care. IMPLICATIONS FOR NURSING AND HEALTH POLICIES: Nurses have the right to work safely, and when the shortage of personal protective equipment and nurses during an emergency threatens healthcare quality worldwide, policymakers are challenged to act by establishing an effective allocation of resources for consistent compliance with standard precautions. Moreover, nurses should actively engage in the implementation of infection control policies to improve safe behaviours among citizens and students accessing hospitals.

Coupling Machine Learning Models with Innovative Technology-based Screening Tool for Identifying Psychological Distress among Aboriginal Perinatal Mothers.

Aboriginal perinatal mothers are at a significant risk of experiencing mental health problems, which can have profound negative impacts, despite their overall resilience. This work aimed to build prediction models for identifying high psychological distress among Aboriginal perinatal mothers by coupling machine learning models with an innovative and culturally-safe screening tool. The original dataset of 179 Aboriginal mothers with 337 variables was obtained from twelve perinatal health settings at Perth metropolitan and regional centers in Western Australia between July and September 2022, using a specifically designed web-based rubric for the perinatal mental health assessment. After data preprocessing and feature selection, 23 variables related to emotional manifestations, the problematic partner, worries about daily living, and the need for follow-up wraparound support were identified as significant predictors for the high risk of psychological distress measured by the Kessler 5 plus adaptation. The selected predictors were used to train prediction models, and most of the chosen machine learning models achieved satisfactory results, with Random Forest and Support Vector Machine yielding the highest AUC of over 0.95, accuracy over 0.86, and F1 score above 0.87. This study demonstrates the potential of using machine learning-based models in clinical decision-making to facilitate healthcare and social and emotional well-being for Aboriginal families.

A deep multi-view imbalanced learning approach for identifying informative COVID-19 tweets from social media.

Social media platforms such as Twitter are home ground for rapid COVID-19-related information sharing over the Internet, thereby becoming the favorable data resource for many downstream applications. Due to the massive pile of COVID-19 tweets generated every day, it is significant that the machine-learning-supported downstream applications can effectively skip the uninformative tweets and only pick up the informative tweets for their further use. However, existing solutions do not specifically consider the negative effect caused by the imbalanced ratios between informative and uninformative tweets in training data. In particular, most of the existing solutions are dominated by single-view learning, neglecting the rich information from different views to facilitate learning. In this study, a novel deep imbalanced multi-view learning approach called D-SVM-2K is proposed to identify the informative COVID-19 tweets from social media. This approach is built upon the well-known multiview learning method SVM-2K to incorporate different views generated from different feature extraction techniques. To battle against the class imbalance problem and enhance its learning ability, D-SVM-2K stacks multiple SVM-2K base classifiers in a stacked deep structure where its base classifiers can learn from either the original training dataset or the shifted critical regions identified using the well-known k-nearest neighboring algorithm. D-SVM-2K also realises a global and local deep ensemble learning on the multiple views' data. Our empirical experiments on a real-world labeled tweet dataset demonstrate the effectiveness of D-SVM-2K in dealing with the real-world multi-view class imbalance issues.

Somatic acupressure for the fatigue-sleep disturbance-depression symptom cluster in breast cancer survivors: A phase II randomized controlled trial.

OBJECTIVE: To evaluate the feasibility of the somatic acupressure (SA) for managing the fatigue-sleep disturbance-depression symptom cluster (FSDSC) among breast cancer (BC) survivors and its preliminary effects. METHODS: In this Phase II randomized controlled trial (RCT), 51 participants were randomised evenly into the true SA group, sham SA group, and usual care group. All the participants received usual care. The two SA groups performed additional true or sham self-acupressure daily for seven weeks. The primary outcomes related to the assessment of participants' recruitment and compliance with study questionnaires and interventions. Clinical outcomes assessed the preliminary effects of SA on fatigue, sleep disturbance, depression, and quality of life. Semi-structured interviews were undertaken to capture participants' experiences of participating in this study. The statistical effects of the intervention on the outcomes were modelled in repeated measures ANOVA and adjusted generalized estimating equations. RESULTS: Forty-five participants completed the SA intervention. No adverse events were reported. Over 85% of the participants could sustain for 25 days or more and 15 min or more per session, but the adherence to the intervention requirement was yet to improve. The group by time effect of the FSDSC and depression were significant (p < 0.05). Qualitative findings showed that participants positively viewed SA as a beneficial strategy for symptom management. CONCLUSIONS: The SA intervention protocol and the trial procedures were feasible. The results demonstrated signs of improvements in targeted outcomes, and a full-scale RCT is warranted to validate the effects of SA on the FSDSC.

A Novel AUC Maximization Imbalanced Learning Approach for Predicting Composite Outcomes in COVID-19 Hospitalized Patients.

The COVID-19 patient data for composite outcome prediction often comes with class imbalance issues, i.e., only a small group of patients develop severe composite events after hospital admission, while the rest do not. An ideal COVID-19 composite outcome prediction model should possess strong imbalanced learning capability. The model also should have fewer tuning hyperparameters to ensure good usability and exhibit potential for fast incremental learning. Towards this goal, this study proposes a novel imbalanced learning approach called Imbalanced maximizing-Area Under the Curve (AUC) Proximal Support Vector Machine (ImAUC-PSVM) by the means of classical PSVM to predict the composite outcomes of hospitalized COVID-19 patients within 30 days of hospitalization. ImAUC-PSVM offers the following merits: (1) it incorporates straightforward AUC maximization into the objective function, resulting in fewer parameters to tune. This makes it suitable for handling imbalanced COVID-19 data with a simplified training process. (2) Theoretical derivations reveal that ImAUC-PSVM has the same analytical solution form as PSVM, thus inheriting the advantages of PSVM for handling incremental COVID-19 cases through fast incremental updating. We built and internally and externally validated our proposed classifier using real COVID-19 patient data obtained from three separate sites of Mayo Clinic in the United States. Additionally, we validated it on public datasets using various performance metrics. Experimental results demonstrate that ImAUC-PSVM outperforms other methods in most cases, showcasing its potential to assist clinicians in triaging COVID-19 patients at an early stage in hospital settings, as well as in other prediction applications.

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.

BACKGROUND: We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS: This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS: Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION: The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

Development of generic student engagement scale in higher education: An application on healthcare students.

AIM: Student engagement is an important factor to the success of higher education. This study aimed to develop a Generic Student Engagement Scale (GSES) for face-to-face and online learning. DESIGN: This was a cross-sectional psychometric study. METHODS: We tested the psychometric properties of GSES in 451 students at the school of nursing and health studies undertaking online and face-to-face learning at a local university in Hong Kong between 2016 and 2018. RESULTS: Content validity, face validity and test-retest reliability of GSES were satisfactory. The 29-item GSES contains five factors "self-regulated learning," "cognitive strategy use," "experienced emotion," "teacher-student interaction," and "enjoyment of school life" with the good model fit. The GSES is a reliable and valid psychometric instrument to measure student engagement in face-to-face and online learning among undergraduates and higher diploma students. Our results implied that student engagement can be assessed in routine or research by using our instrument.

The effect of an evidence-based Tai chi intervention on the fatigue-sleep disturbance-depression symptom cluster in breast cancer patients: A preliminary randomised controlled trial.

PURPOSE: To explore the potential effects of Tai chi on the fatigue-sleep disturbance-depression symptom cluster (FSDSC) among breast cancer (BC) patients. METHODS: This study was conducted as a preliminary randomized controlled trial among 72 BC patients (36 Tai chi and 36 control participants). All the participants were provided with routine care, while participants in the Tai chi group received an additional 8-week Tai chi intervention. Participants' fatigue, sleep disturbance and depression were assessed by the Brief Fatigue Inventory, the Pittsburgh Sleep Quality Index, and the Hospital Anxiety and Depression Scale-Depression. Participants' quality of life (QoL) was assessed by the Functional Assessment of Cancer Therapy-Breast. Both covariates-unadjusted and adjusted GEE models were run to assess the effects of Tai chi intervention on the FSDSC and QoL and the relevant impacts of the covariates. RESULTS: Sixty-nine participants completed this study. In the unadjusted GEE model, compared with the control group and baseline, participants in the Tai chi group showed significant reductions in fatigue (p < 0.001), sleep disturbance (p < 0.001) and depression (p = 0.006), as well as a significant improvement in QoL (p = 0.032) at immediately post-intervention and four-week follow-up. The positive regression coefficients of the adjusted GEE model showed fatigue, sleep disturbance and depression can have impacts on each other (all at p < 0.05). CONCLUSION: Tai chi as an adjuvant intervention to routine care could relieve the symptom cluster of fatigue, sleep disturbance and depression and improve QoL among BC patients.

Effects of the School-Based Integrated Health Promotion Program With Hydroponic Planting on Green Space Use and Satisfaction, Dietary Habits, and Mental Health in Early Adolescent Students: A Feasibility Quasi-Experiment.

Background: School-based green space activities have been found to be beneficial to the physical activity level and lifestyle habits of adolescent students. However, their effects on green space use and satisfaction, mental health, and dietary behaviors required further investigation. This study aimed to investigate the effects of school-based hydroponic planting integrated with health promotion activities in improving green space use, competence and satisfaction, healthy lifestyle, mental health, and health-related quality of life (QoL) among early adolescent students in secondary schools. Methods: This study adopted a three-group comparison design (one control and two intervention groups). Secondary school students (N = 553) of grades 7-9 participated in either (1) hydroponic planting (two times per week for 8 months) integrated with health promotion activities; (2) only health promotion activities (one time per week for 6 weeks); or (3) control group. Outcomes assessed by questionnaire included green space use and satisfaction, life happiness, lifestyle, depressive symptoms, and health-related QoL. Results: After adjusting for sex and school grade, the scores in "green space distance and use" and "green space activity and competence" were significantly better in the intervention groups than in the control group. Hydroponic planting integrated with health promotion activities was also associated with better scores in dietary habits and resistance to substance use. Intervention groups had a higher score in "Green space sense and satisfaction" and life happiness when compared with the control group. Conclusions: Our study shows that the school-based hydroponic planting integrated with health promotion activities were feasible and, to a certain extent, useful to improve green space use and competence, dietary habits, and resistance to substance use among early adolescent students in secondary schools in urban areas. Future studies should address the limitations identified, for example, designing a randomized controlled trial that could fit school schedules to generate new evidence for physical and mental health in adolescent communities.

Faculty Service-Learning Students as Home-Visitors: Outcomes of a Lifestyle Modification Program for Vulnerable Families With Residents in Rural Indonesian Communities.

Background: Risks attributed to chronic diseases, cancer, musculoskeletal discomfort, and infectious diseases among Indonesians were found to be associated with lifestyle behaviors, particularly in rural areas. The aim of this study was to examine the outcomes of a home-visiting lifestyle modification program on improving health risk behaviors among Indonesians living in rural areas. Methods: A total of 160 Indonesians living in rural hamlets in the Yogyakarta Region of Indonesia participated in the program in the period of June 21 to July 21, 2019. In the pre-intervention home interview, learning needs of diet, exercise, hand hygiene, and substance use were identified by using structured assessment tools. In the next home visit, the visitors provided health education and facilitated lifestyle planning based on the related affective and cognitive domains of learning. Subsequent follow-up interviews were conducted 3 weeks after intervention. Results: The results showed that the self-reported intake of vegetables, fruits, meat and salt, cooking with less oil, hand hygiene before eating, number of cigarettes smoked, and symptoms of muscle stiffness significantly improved after the intervention. The lifestyle modification program consisted of the affective and cognitive domains of learning, and could lead to the target behavioral changes in self-reported and observable measures over 1 month. Conclusions: The findings contributed to the framework of community-based health education for health risk reduction and behavioral modification in developing rural communities where health care resources were limited. Further studies with control groups and vigorous objective measures were recommended to elucidate its long-term impacts. The factors leading to its sustainability concerning collaborative care partnerships between community residents and faculty resources are worthy of continued exploration.

Erratum: Lee, R.L.T., et al. Effects of an Unstructured Free Play and Mindfulness Intervention on Wellbeing in Kindergarten Students. Int. J. Environ. Res. Public Health 2020, 17, 5382.

The authors wish to make the following corrections to their paper [...].

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis.

BACKGROUND: COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. OBJECTIVE: This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. METHODS: We collected 31,100 English tweets containing COVID-19 vaccine-related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. RESULTS: Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. CONCLUSIONS: Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.

Systematic review of the impact of unstructured play interventions to improve young children's physical, social, and emotional wellbeing.

This review aimed to examine the effectiveness of unstructured play interventions on young children's physical, emotional and social wellbeing in various community settings. Eligibility criteria of articles included (1) studies which included young children aged three to seven years; (2) intervention studies which involved unstructured, free or loose parts play; (3) experimental or randomized controlled trial designs, with or without random allocation to groups; and (4) target variables of the study should include measurable physical, social or psychological constructs as modifiable outcomes. Electronic searches were conducted from June 2018 to March 2019 in ERIC, MEDLINE, PubMed, ProQuest, Sage Publications, Web of Science, Scopus, and Sociological Abstracts. Data were extracted from the included studies independently by using a pilot form. The study outcome measures of unstructured play in the eight selected articles were categorized into three aspects of children's physical health, social skills and emotional wellbeing. All studies reported positive impacts on children's physical activity level, social engagement and emotional wellbeing. We conclude that our review with identified impacts would assist future research directions and policy implementation in this promising field..

Smart Device Use and Perceived Physical and Psychosocial Outcomes among Hong Kong Adolescents.

Excessive electronic screen-based activities have been found to be associated with negative outcomes. The aim of this study was to investigate the prevalences and patterns of smart device activities and the purposes and perceived outcomes related to smart device use, and the differences in patterns of smart device activities between adolescents who did and did not perceive these outcomes. The study was a cross-sectional survey of Hong Kong primary and secondary school students. Demographic characteristics, purpose and pattern of the activities, and frequencies of the outcomes were measured. Data from 960 adolescents aged 10-19 were analyzed. Nearly 86% of the sample use smart device daily. The one-week prevalence of perceived sleep deprivation, eye discomfort, musculoskeletal discomfort, family conflict and cyberbullying victimization related to smart device use were nearly 50%, 45%, 40%, 20% and 5% respectively. More than 25% of the respondents were at risk of negative outcomes related to smart device activities for more than 1 h per day, browsing and gaming on at least 4 days per week and watching TV/movies and posting on more than 2 days per week. Their patterns of smart device activities may put a significant number of them at risk of negative outcomes.