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This study aimed to explore the mediating effect of self-management (SM) on the relationship between illness perception and quality of life (QOL) among Human immunodeficiency virus (HIV)-positive men who have sex with men (MSM).
A cross-sectional study.
We explored the effect of illness perception and self-management on QOL using the multiple regression model. Moreover, we conducted a simple mediation analysis to examine the role of SM in the relationship between illness perception and QOL. In addition, a parallel mediation analysis was performed to investigate the differences in domains of SM on the relationship between illness perception and QOL.
Among 300 Chinese HIV-positive MSM, the mean score of SM was 39.9 ± 6.97, with a range of 14.0–54.0. The higher score in SM indicated a higher level of HIV SM. SM was negatively related to illness perception (r = −0.47) while positively related to QOL (r = 0.56). SM partially mediated the relationship between illness perception and QOL, accounting for 25.3% of the total effect. Specifically, both daily self-management health practices and the chronic nature of the self-management domain played a parallel role in mediating the relationship between illness perception and QOL.
Our study demonstrated that SM was a significant factor influencing QOL among HIV-positive MSM. Focusing on daily self-management health practices and the chronic nature of self-management could be the potential key targets for enhancing HIV self-management strategies.
This study emphasized the role of SM in the well-being of HIV-positive MSM and underscored the importance of developing interventions that integrate SM strategies to improve QOL in this population.
No patient or public contribution.
To examine the relationships between nurses' exposure to workplace violence and self-reports of workplace cognitive failure.
A cross-sectional study.
An online questionnaire was administered in April 2023 to nurses in Michigan, US. Structural equation modelling was used to examine effects of physical and non-physical workplace violence (occupational stressors) and work efficiency and competence development (occupational protective factors) on workplace cognitive failure.
Physical violence was a significant predictor of the action subscale of cognitive failure. There were no direct effects of non-physical violence, workplace efficiency, or competence development on any of the workplace cognitive failure dimensions. Both types of violence and efficiency had significant indirect effects on workplace cognitive failure via work-related exhaustion. Work-related exhaustion predicted significantly higher scores for workplace cognitive failure.
Workplace violence and work efficiency exhibited primarily indirect effects on workplace cognitive failure among nurses via work-related exhaustion.
Nurses experiencing workplace violence may be at increased risk for workplace cognitive failure, especially if they are also experiencing work-related exhaustion. Workplaces that nurses perceive as more efficient can help to mitigate the effects of violence on nurses' cognitive failure.
This study addressed the possible effects of workplace violence as well as work efficiency and competence development on nurses' cognitive failure at work. Analyses revealed primarily indirect effects of workplace violence, and indirect protective effects of work efficiency, on nurses' cognitive failure via work-related exhaustion. This research has implications for healthcare organizations and suggests that efforts made by healthcare workplaces to prevent violence and work-related exhaustion, and to enhance work efficiency, may help to mitigate workplace cognitive failure among nurses.
We have followed the STROBE checklist in reporting this study.
No Patient or public contribution.
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.
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. 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.
As the awareness on violent deaths from guns, drugs, and suicides emerges as a public health crisis in the United States, attempts to prevent injury and mortality through nursing research are critical. The National Violent Death Reporting System provides public health surveillance of US violent deaths; however, understanding the National Violent Death Reporting System's research utility is limited. The purpose of our rapid review of the 2019-2023 literature was to understand to what extent artificial intelligence methods are being used with the National Violent Death Reporting System. We identified 16 National Violent Death Reporting System artificial intelligence studies, with more than half published after 2020. The text-rich content of National Violent Death Reporting System enabled researchers to center their artificial intelligence approaches mostly on natural language processing (50%) or natural language processing and machine learning (37%). Significant heterogeneity in approaches, techniques, and processes was noted across the studies, with critical methods information often lacking. The aims and focus of National Violent Death Reporting System studies were homogeneous and mostly examined suicide among nurses and older adults. Our findings suggested that artificial intelligence is a promising approach to the National Violent Death Reporting System data with significant untapped potential in its use. Artificial intelligence may prove to be a powerful tool enabling nursing scholars and practitioners to reduce the number of preventable, violent deaths. The last-minute cancellation of surgeries profoundly affects patients and their families. This research aimed to forecast these cancellations using EMR data and meteorological conditions at the time of the appointment, using a machine learning approach. We retrospectively gathered medical data from 13 440 pediatric patients slated for surgery from 2018 to 2021. Following data preprocessing, we utilized random forests, logistic regression, linear support vector machines, gradient boosting trees, and extreme gradient boosting trees to predict these abrupt cancellations. The efficacy of these models was assessed through performance metrics. The analysis revealed that key factors influencing last-minute cancellations included the impact of the coronavirus disease 2019 pandemic, average wind speed, average rainfall, preanesthetic assessments, and patient age. The extreme gradient boosting algorithm outperformed other models in predicting cancellations, boasting an area under the curve value of 0.923 and an accuracy of 0.841. This algorithm yielded superior sensitivity (0.840), precision (0.837), and F1 score (0.838) relative to the other models. These insights underscore the potential of machine learning, informed by EMRs and meteorological data, in forecasting last-minute surgical cancellations. The extreme gradient boosting algorithm holds promise for clinical deployment to curtail healthcare expenses and avert adverse patient-family experiences. This study aimed to identify the main themes from exit interviews of adult patients with type 2 diabetes after completion of a diabetes education program. Eighteen participants with type 2 diabetes completed an exit interview regarding their program experience and satisfaction. Semistructured interview questions were used, and the interviews were auto-recorded. The interview transcripts were preprocessed and analyzed using four natural language processing–based text-mining techniques. The top 30 words from the term frequency and term frequency–inverse document frequency each were derived. In the N-gram analysis, the connection strength of “diabetes” and “education” was the highest, and the simultaneous connectivity of word chains ranged from a maximum of seven words to a minimum of two words. Based on the CONvergence of iteration CORrelation (CONCOR) analysis, three clusters were generated, and each cluster was named as follows: participation in a diabetes education program to control blood glucose, exercise, and use of digital devices. This study using text mining proposes a new and useful approach to visualize data to develop patient-centered diabetes education. Vector borne diseases (VBDs) present significant public health challenges in Southeast Asia (SEA), and the increasing number of cases threatens vulnerable communities. Inadequate vector control and management have been linked to the spread of VBDs. To address these issues, community participation has been proposed as a promising approach to enhance health programmes and control of VBDs. This article outlines a protocol for a scoping review of the published literature on community-participation approaches to control VBDs in the SEA region. The primary research question is ‘How does community participation complement the control of VBDs in SEA?’ This review aims to provide an overview of various approaches and identify barriers and facilitators to effective implementation.
The research questions will guide the scoping review. In stage 1, peer-reviewed publications from PubMed, Web of Science and Scopus will be searched using predefined search terms related to community-based approaches and VBDs in the SEA region, English, Indonesian and Malay published between 2012 and 2022. In stage 2, the references from relevant articles will be screened for eligibility. In stage 3, eligible articles will be charted in Microsoft Excel to facilitate the review process, and studies will be characterised based on the investigated diseases; this review will also highlight the methodological context of these studies. In stage 4, a thematic analysis will be conducted to derive meaningful findings from the dataset relevant to the research inquiry, followed by writing the results in stage 5. This scoping review aims to be the first to explore community participation in VBD control in the SEA population, providing valuable insights for future research and stakeholders involved in disease control.
This scoping review does not require ethical approval because the methodology synthesises information from available articles. This review is planned for dissemination in academic journals, conference presentations and shared with stakeholders as part of knowledge sharing among those involved in VBD control.
More than 170 countries have implemented disability-targeted social protection programmes, although few have been rigorously evaluated. Consequently, a non-randomised controlled trial is being conducted of a pilot ‘cash-plus’ programme implemented by UNICEF Laos and the Laos government for children with disabilities in the Xiengkhouang Province in Laos. The intervention combines a regular cash transfer with provision of assistive devices and access for caregivers to a family support programme.
The non-randomised controlled trial will involve 350 children with disabilities across 3 districts identified by programme implementers as eligible for the programme (intervention arm). Implementers have also identified approximately 180 children with disabilities in neighbouring districts, who would otherwise meet eligibility criteria but do not live in the project areas (control arm). The trial will assess the impact of the programme on child well-being (primary outcome), as well as household poverty, caregiver quality of life and time use (secondary outcomes). Baseline data are being collected May–October 2023, with endline 24 months later. Analysis will be intention to treat. A complementary process evaluation will explore the implementation, acceptability of the programme, challenges and enablers to its delivery and mechanisms of impact.
The study has received ethical approval from the London School of Hygiene and Tropical Medicine and the National Ethics Committee for Health Research in Laos. Informed consent and assent will be taken by trained data collectors. Data will be collected and stored on a secure, encrypted server and its use will follow a detailed data management plan. Findings will be disseminated in academic journals and in short briefs for policy and programmatic actors, and in online and in-person events.
There was no evidence regarding the relationship between septic shock and tracheal injury scores. Investigate whether septic shock was independently associated with tracheal injury scores in intensive care unit (ICU) patients with invasive ventilation.
Prospective observational cohort study.
Our study was conducted in a Class III hospital in Hebei province, China.
Patients over 18 years of age admitted to the ICU between 31 May 2020 and 3 May 2022 with a tracheal tube and expected to be on the tube for more than 24 hours.
Tracheal injuries were evaluated by examining hyperaemia, ischaemia, ulcers and tracheal perforation by fiberoptic bronchoscope. Depending on the number of lesions, the lesions were further classified as moderate, severe or confluent.
Among the 97 selected participants, the average age was 56.6±16.5 years, with approximately 64.9% being men. The results of adjusted linear regression showed that septic shock was associated with tracheal injury scores (β: 2.99; 95% CI 0.70 to 5.29). Subgroup analysis revealed a stronger association with a duration of intubation ≥8 days (p=0.013).
Patients with septic shock exhibit significantly higher tracheal injury scores compared with those without septic shock, suggesting that septic shock may serve as an independent risk factor for tracheal injury.
ChiCTR2000037842, registered 03 September 2020. Retrospectively registered, https://www.chictr.org.cn/edit.aspx?pid=57011&htm=4.
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