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This study aims to investigate the epidemiological characteristics of COVID-19 infection among healthcare workers, including the severity, duration of infection, post-infection symptoms and related influencing factors.
A self-administered questionnaire was utilized to assess the post-infection status of primary healthcare workers in Jiangsu Province. The questionnaire collected information on demographic characteristics, lifestyle habits, post-infection clinical manifestations, work environment and recovery time of the respondents. Customized outcome events were selected as dependent variables and logistic regression models were employed to analyse the risk factors. Phi-coefficient was used to describe the relationship between post-infection symptoms.
The analysis revealed that several factors, such as female, older age, obesity, previous medical history, exposure to high-risk environments and stress, were associated with a higher likelihood of experiencing more severe outcomes. On the other hand, vaccination and regular exercise were found to contribute to an earlier resolution of the infection. Among the post-infection symptoms, cough, malaise and muscle aches were the most frequently reported. Overall, there was a weak association among symptoms persisting beyond 14 days, with only cough and malaise, malaise and dizziness and headache showing a stronger correlation.
The study findings indicate that the overall severity of the first wave of infection, following the complete lifting of restrictions in China, was low. The impact on primary healthcare workers was limited, and the post-infection symptoms exhibited similarity to those observed in other countries. It is important to highlight that these conclusions are specifically relevant to the population infected with the Omicron variant.
This study helps to grasp the impacts of the first wave of COVID-19 infections on healthcare workers in China after the national lockdown was lifted.
Primary healthcare workers in Jiangsu Province, including doctors, nurses, pharmacists and other personnel from primary healthcare units such as community health service centres and health centres.
Examine profiles of safety attitudes among novices and explore whether profiles moderate the occupational identity–turnover pathway.
Novice nurses face unique challenges in adopting positive safety attitudes, which influence outcomes like turnover. However, past research found only average levels of safety attitudes among novices, ignoring possible heterogeneity. Exploring whether meaningful subgroups exist based on safety perspectives and factors shaping them can provide insights to improve safety attitudes and retention.
This study was designed as a cross-sectional investigation.
Data were collected through the distribution of questionnaires. Descriptive statistics were first conducted, followed by latent profile analysis. We then carried out univariate analysis and ordinal multinomial regression to explore the factors shaping the different profiles. Finally, we examine the moderating effect of nurses' safety attitudes with different latent profiles on the relationship between professional identification and turnover intention.
A total of 816 novice nurses were included. Three profiles were identified: high, moderate and low safety attitudes – higher attitudes were associated with lower turnover intention. Interest in nursing, health status, identity and turnover predicted profile membership. Moderate profile had a stronger buffering effect on the identity–turnover link versus high profile.
Multiple safety attitude profiles exist among novice nurses. Certain factors like interest in nursing and occupational identity are associated with more positive safety profiles. Targeting these factors could potentially improve safety attitudes and reduce turnover among novice nurses. The moderating effects suggest that tailored interventions matching specific subgroups may maximize impact.
Assessing subgroup attitudes enables tailored training for novices' specific needs, nurturing continuous improvement. Supporting early career development and role identity may strengthen retention intentions.
Nurses, assuming a wide range of clinical and patient care responsibilities in a healthcare team, are highly susceptible to direct and indirect exposure to traumatic experiences. However, literature has shown that nurses with certain traits developed a new sense of personal strength in the face of adversity, known as post-traumatic growth (PTG). This review aimed to synthesize the best available evidence to evaluate personal and work-related factors associated with PTG among nurses.
Mixed studies systematic review.
Studies examining factors influencing PTG on certified nurses from all healthcare facilities were included. Published and unpublished studies were identified by searching 12 databases from their inception until 4th February 2023. Two reviewers independently screened, appraised, piloted a data collection form, and extracted relevant data. Meta-summary, meta-synthesis, meta-analysis, as well as subgroup and sensitivity analyses were performed. Integration of results followed result-based convergent design.
A total of 98 studies with 29,706 nurses from 18 countries were included. These included 49 quantitative, 42 qualitative, and seven mixed-methods studies. Forty-six influencing factors were meta-analyzed, whereas nine facilitating factors were meta-summarized. A PTG conceptual map was created. Four constructs emerged from the integration synthesis: (a) personal system, (b) work-related system, (c) event-related factors, and (d) cognitive transformation.
The review findings highlighted areas healthcare organizations could do to facilitate PTG in nurses. Practical implications include developing intervention programs based on PTG facilitators. Further research should examine the trend of PTG and its dynamic response to different nursing factors.
Research on trauma-focused therapies targeting nurses' mental health is lacking. Therefore, findings from this review could inform healthcare organizations on the PTG phenomenon and developing support measures for nurses through healthcare policies and clinical practice.
Optimal selection of vascular access devices is based on multiple factors and is the first strategy to reduce vascular access device-related complications. This process is dependent on behavioural and human factors. The COM-B (Capability, Opportunity, Motivation, Behaviour) model was used as a theoretical framework to organize the findings of this systematic review.
To synthesize the evidence on determinants shaping the optimal selection of vascular access devices, using the COM-B behavioural model as the theoretical framework.
Systematic review of studies which explore decision-making at the time of selecting vascular access devices.
The Medline, Web of Science, Scopus and EbscoHost databases were interrogated to extract manuscripts published up to 31 December 2021, in English or Spanish.
Among 16 studies included in the review, 8/16 (50%) focused on physical capability, 8/16 (50%) psychological capability, 15/16 (94%) physical opportunity, 12/16 (75%) social opportunity, 1/16 (6%) reflective motivation and 0/16 (0%) automatic motivation. This distribution represents a large gap in terms of interpersonal and motivational influences and cultural and social environments. Specialist teams (teams created for the insertion or maintenance of vascular access devices) are core for the optimal selection of vascular access devices (75% physical capability, 62% psychological capability, 80% physical opportunity and 100% social opportunity).
Specialist teams predominantly lead all actions undertaken towards the optimal selection of vascular access devices. These actions primarily centre on assessing opportunity and capability, often overlooking motivational influences and social environments.
A more implementation-focused professional approach could decrease inequity among patients and complications associated with vascular access devices.
Optimal selection of vascular access devices is the primary strategy in mitigating complications associated with these devices. There is a significant disparity between interpersonal and motivational influences and the cultural and social environments. Furthermore, specialized teams play a pivotal role in facilitating the optimal selection of vascular access devices. The study can benefit institutions concerned about vascular access devices and their complications.
This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
No Patient or Public Contribution.
Optimal selection of vascular devices remains a growing yet unresolved issue with costly clinical and patient experience impact. Interventions to improve the optimal selection of vascular devices have focused on training, education, algorithms and implementation of specialist vascular teams; alas, these approaches do not seem to have substantially addressed the problem. Specialist vascular teams should evolve and pivot towards leading the implementation of quality improvement interventions, optimizing resource use and enhancing their role.
Implementation science helps generate approaches to expedite the uptake of evidence in practice. Mixed methods are commonly used in implementation research because they allow researchers to integrate distinct qualitative and quantitative methods and data sets to unravel the implementation process and context and design contextual tools for optimizing the implementation. To date, there has been limited discussion on how to ensure rigor in mixed methods implementation research.
To present Particularity, Engagement, Actionable Inferences, Reflexivity, and Legitimation (PEARL) as a practical tool for understanding various components of rigor in mixed methods implementation research.
This methodological discussion is based on a nurse-led mixed methods implementation study. The PEARL tool was developed based on an interpretive, critical reflection, and purposive reading of selected literature sources drawn from the researchers' knowledge, experiences of designing and conducting mixed methods implementation research, and published methodological papers about mixed methods, implementation science, and research rigor.
An exemplar exploratory sequential mixed methods study in nursing is provided to illustrate the application of the PEARL tool. The proposed tool can be a useful and innovative tool for researchers and students intending to use mixed methods in implementation research. The tool offers a straightforward approach to learning the key rigor components of mixed methods implementation research for application in designing and conducting implementation research using mixed methods.
Rigorous implementation research is critical for effective uptake of innovations and evidence-based knowledge into practice and policymaking. The proposed tool can be used as the means to establish rigor in mixed methods implementation research in nursing and health sciences.
Cancer screening is a pivotal method for reducing mortality from disease, but the screening coverage is still lower than expected. Telehealth interventions demonstrated significant benefits in cancer care, yet there is currently no consensus on their impact on facilitating cancer screening or on the most effective remote technology.
A network meta-analysis was conducted to detect the impact of telehealth interventions on cancer screening and to identify the most effective teletechnologies.
Six English databases were searched from inception until July 2023 to yield relevant randomized controlled trials (RCTs). Two individual authors completed the literature selection, data extraction, and methodological evaluations using the Cochrane Risk of Bias tool. Traditional pairwise analysis and network meta-analysis were performed to identify the overall effects and compare different teletechnologies.
Thirty-four eligible RCTs involving 131,644 participants were enrolled. Overall, telehealth interventions showed statistically significant effects on the improvement of cancer screening. Subgroup analyses revealed that telehealth interventions were most effective for breast and cervical cancer screening, and rural populations also experienced benefits, but there was no improvement in screening for older adults. The network meta-analysis indicated that mobile applications, video plus telephone, and text message plus telephone were associated with more obvious improvements in screening than other teletechnologies.
Our study identified that telehealth interventions were effective for the completion of cancer screening and clarified the exact impact of telehealth on different cancer types, ages, and rural populations. Mobile applications, video plus telephone, and text message plus telephone are the three forms of teletechnologies most likely to improve cancer screening. More well-designed RCTs involving direct comparisons of different teletechnologies are needed in the future.
Telehealth interventions should be encouraged to facilitate cancer screening, and the selection of the optimal teletechnology based on the characteristics of the population is also necessary.
Both nurses' well-being and quality of care are top priorities of the healthcare system. Yet, there is still a gap in understanding the extent and how authentic leadership influences them. This information is needed to inform the development of effective interventions, organizational practices, and policies. Thus, this study aimed to test the mechanism by which nurses' perception of their managers' authentic leadership impacts nurses' well-being and perception of quality of care, given the role of the nursing practice environment and nurses' psychological capital.
A cross-sectional design was used.
This study recruited a random sample of 680 nurses from six hospitals in Saudi Arabia. A final sample of 415 completed the surveys, with a response rate of 61%. Structural equation modeling was performed to test the hypothesized model.
The study showed that nurses' perceptions of authentic leadership in their managers positively and directly affect their perceptions of quality of care but do not directly affect nurses' well-being. Both the nursing practice environment and psychological capital fully mediated the relationship between authentic leadership and nurses' well-being. However, the nursing practice environment partially mediated the relationship between authentic leadership and perceptions of quality of care.
The findings contribute to understanding the crucial role of authentic leaders' style in nurses' well-being and quality of care through its positive impact on the nursing practice environment and psychological capital.
Designing interventions and policies that specifically target nursing managers' authentic leadership style has implications for enhancing nurses' well-being and the quality of patient care. Institutional measures are needed to help leaders practice an authentic leadership style to create a positive nursing practice environment and cultivate nurses' psychological capital, both of which contribute to nurses' well-being and attaining a better quality of care. Further work is required to highlight the outcomes of implementing an authentic leadership style relevant to other leadership styles.
The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, the review aimed to explore whether implementing these interventions as bundles reduced practice pattern variance and positively impacted patient care outcomes for inpatients with T2DM. Six databases were searched for articles published from January 1, 2000, to January 1, 2024. Nine studies met criteria and were summarized by aims, outcome measures, clinical or practice implications, AI/ML model types, study variables, and AI/ML model outcomes. A variety of AI/ML models were used. Multiple data sources were leveraged to train the models, resulting in varying impacts on practice patterns and outcomes. Studies included aims across 4 thematic areas to address: therapeutic patterns of care, analysis of treatment pathways and their constraints, dashboard development for clinical decision support, and medication optimization and prescription pattern mining. Multiple disparate data sources (i.e., prescription payment data) were leveraged outside of those traditionally available within EHR databases. Notably missing was the use of holistic multidisciplinary data (i.e., nursing and ancillary) to train AI/ML models. AI/ML can assist in identifying the appropriateness of specific interventions to manage diabetic care and support adherence to efficacious treatment pathways if the appropriate data are incorporated into AI/ML design. Additional data sources beyond the EHR are needed to provide more complete data to develop AI/ML models that effectively discern meaningful clinical patterns. Further study is needed to better address nursing care using AI/ML to support effective inpatient diabetes management. We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative technologies within their workflow. Major electronic health record vendors have begun to leverage large language models to process and analyze vast amounts of clinical natural language text, performing a wide range of tasks in healthcare settings to help alleviate clinicians' burden. Although such technologies can be helpful in applications such as patient education, drafting responses to patient questions and emails, medical record summarization, and medical research facilitation, there are concerns about the tools' readiness for use within the healthcare domain and acceptance by the current workforce. The goal of this article is to provide nurses with an understanding of the currently available foundation models and artificial intelligence tools, enabling them to evaluate the need for such tools and assess how they can impact current clinical practice. This will help nurses efficiently assess, implement, and evaluate these tools to ensure these technologies are ethically and effectively integrated into healthcare systems, while also rigorously monitoring their performance and impact on patient care. No abstract available As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths. To assess the level of mental workload (MWL) of intensive care unit (ICU) nurses in performing different human-machine tasks and examine the predictors of the MWL.
A cross-sectional questionnaire study.
Between January and February 2021, data were collected from ICU nurses (n = 427) at nine tertiary hospitals selected from five (east, west, south, north, central) regions in China through an electronic questionnaire, including sociodemographic questions, the National Aeronautics and Space Administration Task Load Index, General Self-Efficacy Scale, Difficulty-assessing Index System of Nursing Operation Technique, and System Usability Scale. Descriptive statistics, t-tests, one-way ANOVA and multiple linear regression models were used.
ICU nurses experienced a medium level of MWL (score 52.04 on a scale of 0–100) while performing human-machine tasks. ICU nurses' MWL was notably higher in conducting first aid and life support tasks (using defibrillators or ventilators). Predictors of MWL were task difficulty, system usability, professional title, age, self-efficacy, ICU category, and willingness to study emerging technology actively. Task difficulty and system usability were the strongest predictors of nearly all typical tasks.
ICU nurses experience a medium MWL while performing human-machine tasks, but higher mental, temporal, and effort are perceived compared to physical demands. The MWL varied significantly across different human-machine tasks, among which are significantly higher: first aid and life support and information-based human-machine tasks. Task difficulty and system availability are decisive predictors of MWL.
This is the first study to investigate the level of MWL of ICU nurses performing different representative human-machine tasks and to explore its predictors, which provides a reference for future research. These findings suggest that healthcare organizations should pay attention to the MWL of ICU nurses and develop customized management strategies based on task characteristics to maintain a moderate level of MWL, thus enabling ICU nurses to perform human-machine tasks better.
No patient or public contribution.
To investigate the relationship between work readiness and work well-being for newly graduated nurses and the mediating role of emotional labor and psychological capital in this relationship.
A cross-sectional survey was conducted in mainland China. A total of 478 newly graduated nurses completed the Work Readiness Scale, Emotional Labour Scale, Psychological Capital Questionnaire, and Work Well-being Scale. Descriptive statistical methods, Pearson correlation analysis, and a structural equation model were used to analyze the available data.
Newly graduated nurses' work readiness was significantly positively correlated with work well-being (r = 0.21, p < 0.01), deep acting (r = 0.11, p < 0.05), and psychological capital (r = 0.18, p < 0.01). Emotional labor and psychological capital partially mediated the relationship between work readiness and work well-being. Additionally, emotional labor and psychological capital had a chain-mediating effect on the association.
Work readiness not only affects newly graduated nurses' work well-being directly but also indirectly through emotional labor and psychological capital. These results provide theoretical support and guidance for the study and improvement of newly graduated nurses' work well-being and emphasize the importance of intervention measures to improve work readiness and psychological capital and the adoption of deep-acting emotional-labor strategies.
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