Journal of Nursing Management最新文献

Background: Chronic kidney disease (CKD) is a global health challenge, and hemodialysis is a common treatment for end-stage renal disease. Patients undergoing hemodialysis often face significant symptom burden, affecting their quality of life. Timely help-seeking behavior (HSB) is crucial for initiating patient-centered communication and effective symptom management. Yet, the specific behavioral patterns and barriers to help-seeking in this group remain poorly understood. However, tools to assess HSB in this population are lacking.

Methods: This multiphase cross-sectional study was conducted across three hospitals in China from August to October 2024, involving 425 participants. The study developed the HSB for hemodialysis symptoms (HSB-HD) scale based on literature reviews, expert consultations, and patient interviews. Psychometric evaluation was performed using classical test theory (CTT) and item response theory (IRT), focusing on reliability, validity (content, construct, criterion-related, discriminant, and convergent), and measurement invariance.

Results: The exploratory factor analysis (EFA) revealed a four-factor structure (symptom detection, symptom interpretation, decision-making for help-seeking, and timely disclosure and action), explaining 72.9% of variance. Confirmatory factor analysis (CFA) showed a good model fit (CFI = 0.964, RMSEA = 0.045). Cronbach's α was 0.953, indicating excellent internal consistency. Validity tests showed significant correlations with the EQ-5D-5L pain, anxiety, and VAS scores. Measurement invariance was confirmed across gender and age groups.

Conclusions: The HSB-HD scale is a reliable and valid tool for assessing HSB in hemodialysis patients. It offers a patient-centered approach to symptom management, providing health care providers with a means to identify those who may benefit from targeted interventions, thereby improving care and quality of life.

Background: Unprofessional behaviour negatively affects staff and patient safety and wellbeing and organisational culture. It typically involves one perpetrator and target/s, as well as staff who may witness, report or respond to the incident, while positive behaviours may buffer experiences. Understanding nurses' experiences across these roles may support reducing unprofessional behaviour. This is the first 360° view of the roles that nurses play in unprofessional behaviour.

Aims: To examine the frequency, type, severity and impact of unprofessional behaviours between nurses and between nurses and medical personnel; the experiences of reporting and responding unprofessional behaviours; and if nurses acknowledge or exhibit positive behaviours.

Method: Secondary analysis of a mixed-method study evaluating an all-staff professional accountability program (Ethos) implemented in eight Australian hospitals. Data included (i) cross-sectional surveys administered pre- and postimplementation (longitudinal investigation of negative behaviour surveys: n = 5178 baseline [n = 2248 nurses] and n = 3975 follow-up [n = 637 nurses] surveys), (ii) interviews with middle managers n = 30 (n = 12 nurses), (iii) 1310 reports of coworker unprofessional behaviours (n = 799 submitted by nurses, n = 538 about nurses) and 1194 reports of coworker positive behaviours (n = 787 by nurses, n = 595 about nurses), and (iv) Ethos messenger surveys n = 60 (n = 17 nurses). Analyses undertaken varied by data type: descriptive analysis for quantitative data and content or thematic analysis for qualitative data.

Results: Nurses exhibited unprofessional behaviours (perpetrators), most commonly towards other nurses (62%-90%) and were the targets of nurses (47%-70%) and medical colleagues (4%-34%). Nurses frequently observed unprofessional behaviour, with 51% witnessed it at least weekly. Many (46%) were not comfortable responding, with 44% believing they would not be considered seriously (reporters). Nurses indicated having the skills (83%) and training (87%) to respond to unprofessional incidents. However, they frequently used workarounds (interview theme) or reported insufficient time. Nurses frequently acknowledged others' positive behaviours (n = 1930, 67%), received positive feedback from nurses (1235 behaviours, 83%) and medical colleagues (94 behaviours, 6%).

Conclusion: Nurses' roles in unprofessional behaviour may include perpetrator, target, observer, reporter, responder and buffer. Individual and organisational-wide approaches are required to confidently address unprofessional behaviours. Multifaceted culture change programs are needed.

Background: The high incidence of nurse presenteeism significantly impact quality of care delivery. Leader-member exchange and clinical leadership have been identified as factors influencing nurses' presenteeism. However, there are few studies investigating the relationship between these variables. This study aimed to investigate the heterogeneity of ambidextrous leadership and its relationships with nurses' presenteeism using latent profile analysis, while also examining the mediating role of leader-member exchange.

Methods: A multicenter cross-sectional survey was conducted from December 2024 to January 2025, involving clinical nurses from three tertiary general hospitals in Guangzhou. Sociodemographic Characteristics Questionnaire, Ambidextrous Leadership Scale, Leader-Member Exchange Scale, and Stanford Presenteeism Scale were used. Latent profile analysis was performed using Mplus 8.3, and mediation effects were tested through PROCESS 4.2 in SPSS 27.0.

Results: A total of 513 valid questionnaires were collected. Three ambidextrous leadership profiles were identified: ambidextrous lagging, vision-oriented, and ambidextrous synergistic. These profiles showed significant differences in the leader-member exchange and presenteeism scores. Moreover, leader-member exchange significantly mediated the relationship between ambidextrous leadership and nurses' presenteeism.

Conclusions: Ambidextrous leadership is heterogeneous. The leader-member exchange mediates ambidextrous leadership and nurses' presenteeism. Therefore, nursing managers should be flexible in changing their leadership styles to reduce nurses' presenteeism.

Implications for nursing management: There should be further intensive training for nursing managers to implement scientifically sound leadership functions, enhance clinical nurse-manager relationships, and increase nurses' motivation, thereby reducing the incidence of presenteeism.

Introduction: Interprofessional simulation enhances both technical and nontechnical skills among healthcare professionals, improving their clinical practice. Co-debriefing, where two facilitators lead debriefing sessions, is a common approach in these trainings. This study aims to assess educational aspects of interprofessional simulation and co-debriefing in postgraduate medical and emergency nursing students.

Methods: A descriptive cross-sectional study with a mixed-methods approach was conducted among postgraduate students (n = 46). A mixed-methods design was used, combining quantitative questionnaire data with qualitative content analysis of open-ended responses, allowing integration of numerical trends with in-depth learner perspectives. A valid and reliable ad hoc questionnaire was designed, and qualitative content analysis was used to examine participants' free-text responses.

Results: Students' perceptions were evaluated across six dimensions: applicability, satisfaction, motivation, safe environment, organization, and co-debriefing. Significant differences were found between medical and nursing students in all dimensions except applicability and organization. Overall, students rated interdisciplinary sessions and co-debriefing positively, though nurses highlighted areas for improvement, such as icebreaker activities, more dynamic simulations, and equal representation of both professions.

Conclusions: While interprofessional simulation and co-debriefing were well received, participants-especially nurses-identified areas for enhancement to ensure a more balanced and engaging learning experience. By integrating quantitative outcomes with qualitative insights, the study highlights when co-debriefing adds value in interprofessional simulation and when single-facilitator approaches may be sufficient.

Starting in 2022 and continuing into 2024, health workers in the UK voted to take widespread strike action. Amongst the literature that has examined healthcare strikes, there is a paucity of qualitative literature and little that explores the perspectives of those who work through strikes. This study sought to address this, exploring the perspectives of health workers who did and did not go on strike in the United Kingdom throughout the 2022-2024 disputes. Semistructured interviews were carried out with an interdisciplinary sample of NHS staff who both did and did not strike. Three themes emerged: reasons for (not) striking, managing the strikes, and views of the NHS. There were several factors that shaped participants' views about and experiences of the strikes. Most participants, even those who did not strike, had sympathy for the strikes and strikers. While the impact of the strikes was not felt equally, with some staff facing fare more acute challenges, many felt that any disruption caused by the strikes was not much worse than a regular busy day in the NHS. Related to this and one point where there was substantial convergence was participants' views of the NHS. Regardless of profession or whether they went on strike, and while all felt a great sense of pride working in the NHS, all saw the NHS as facing multiple and broad crises and the need for substantial reform.

Objectives: Identifying high-performing advanced practice nursing roles and understanding the factors that contribute to their effectiveness are critical for advancing professional development, optimizing workforce deployment, and ensuring long-term sustainability in nursing. This study aimed to (1) identify distinct latent profiles of advanced practice nursing among specialist nurses in mainland China, (2) quantitatively examine the individual and contextual factors associated with high performance, as characterized by these profiles, and (3) qualitatively confirm the significant factors using explanatory semistructured interviews in the high-performance groups.

Methods: A mixed-methods sequential explanatory design was used, in which quantitative data were collected first and subsequently explained through qualitative interviews. Certified specialist nurses from 16 hospitals across urban and rural areas of Shanghai were included. Latent profile analysis (LPA) was conducted using the five domains from the Advanced Practice Role Delineation tool as manifest indicators to classify nurses into distinct performance profiles. Multinomial logistic regression was used to examine potential determinants (e.g., job position) of group membership. Additionally, a backpropagation neural network (BPNN) was developed to rank the importance of contributing factors. Specialist nurses identified as high performers in the quantitative phase were purposively sampled for explanatory semistructured qualitative interviews.

Results: Three latent profiles emerged: high performance (26.1%), moderate performance (46.3%), and low performance (27.6%). Compared to APNs, staff nurses had significantly lower odds of belonging to the high-performance group (β = -1.715, p < 0.001). Nurses with higher professional career ladder (e.g., Level 4) were more likely to be in the high-performance group (β = -1.163, p = 0.042). BPNN analysis identified the professional career ladder and job position as the most influential predictors of high performance. Qualitative findings from interviews with 17 participants reinforced these results, highlighting contextual factors such as leadership support (e.g., formal APN designation, physician endorsement, and organizational recognition) and individual attributes including specialized knowledge, extensive clinical experience, and advanced training.

Conclusion: Identifying the profiles of advanced practice nursing roles provides valuable insights for optimizing APN performance and informing targeted management and policy strategies. High-performing specialist nurses are positioned at the nexus of individual capability, interdisciplinary collaboration, and institutional support.

Background: Nurses in traditional Chinese medicine (TCM) departments face significant sleep challenges associated with occupational stressors. However, person-centered analyses classifying these sleep patterns remain scarce. This study aimed to identify heterogeneous sleep disturbance subgroups via latent profile analysis (LPA) and evaluate the performance of explainable machine learning models in discriminating these subgroups based on demographic and occupational features.

Methods: A cross-sectional survey enrolled 7721 nurses from 130 TCM healthcare institutions in Liaoning Province (December 2024). Data encompassed demographic, occupational, and psychological variables obtained via self-administered questionnaires, including the Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance short form 8a. LPA was employed to categorize sleep disturbance patterns. Recursive feature elimination with random forest (RFE-RF) was used to select features associated with subgroup membership for five machine learning models. Models were trained on 70% of the data and evaluated on a 30% independent test set. The optimal classification model (XGBoost) underwent interpretability analysis using Shapley additive explanations (SHAP).

Results: LPA identified three subgroups: mild-stable (29.8%), moderate-fluctuating (60%), and severe-persistent (10.2%). Machine learning models achieved test AUCs of 0.71-0.84, with XGBoost demonstrating the highest discriminatory performance (AUC = 0.84, 95%CI: 0.83-0.85) in classifying subgroups. SHAP analysis indicated that monthly income, organizational support, hospital level, self-compassion, and resilience were the top five features contributing to the model's classification output.

Conclusion: This study characterized three distinct sleep disturbance subgroups among TCM nurses, with the majority exhibiting moderate symptoms. The sequential application of LPA and explainable machine learning demonstrated robust performance in distinguishing sleep disturbance patterns. Identifying correlates-such as lower income and resilience-may assist nurse managers in stratifying risk and tailoring interventions for those most likely to fall into the severe subgroup. Future longitudinal studies are required to validate the stability of these subgroups and establish causal relationships.

Objectives: Irregular sleep, frequently resulting from night shifts, is associated with various mental health issues. However, the specific associations among these factors remain unclear. This study aimed to investigate the associations of night shift frequency with depressive, anxiety, and comorbid depressive-anxiety symptoms and to estimate the indirect associations through sleep quality and sleep duration.

Methods: A cross-sectional study was conducted among nurses in seven hospitals in Zhejiang Province in 2023. A self-administered questionnaire was utilized to collect sociodemographic, work-related, lifestyle, and mental health information. Multivariable logistic regression models were used to examine the associations of night shift frequency, sleep duration, sleep quality, and mental health outcomes. Additionally, we used a mediation analysis to estimate indirect effects through sleep quality and sleep duration.

Results: A total of 2037 nurses were included in the study. Compared to low-frequency night shifts, high-frequency night shifts were significantly associated with higher odds of depressive symptoms (odds ratio [OR] = 1.46, 95% confidence interval [CI] = 1.20-1.77), anxiety symptoms (OR = 1.29, 95% CI = 1.06-1.58), and comorbid depressive-anxiety symptoms (OR = 1.34, 95% CI = 1.09-1.65). The statistical indirect association via sleep quality accounted for 19.5%, 25.9%, and 19.0% of the total association for depressive, anxiety, and comorbid depressive-anxiety symptoms, respectively. The indirect effect through sleep duration was not statistically significant.

Conclusions: In this cross-sectional study, a higher frequency of night shifts was significantly associated with poorer mental health outcomes among Chinese nurses, with sleep quality serving as a statistical mediator. These findings suggest that optimizing shift scheduling, enhancing institutional support for sleep recovery, and integrating sleep quality monitoring into occupational health policies may be effective strategies to promote nurse well-being. This study provides empirical evidence to inform nursing management practices and health workforce policy.

Aims: To investigate the effects of night shifts (NS) on cerebral oxygenation, cognitive performance, and anxiety in emergency nurses and to examine the role of sleep duration in modulating these outcomes.

Background: NS disrupt circadian rhythms, reduce sleep, and may impair prefrontal cortex function. Emergency nurses, who are exposed to high cognitive and emotional demands, are especially vulnerable to these disruptions, which may compromise psychological well-being, occupational performance, and patient safety.

Method: A prospective, observational, within-subject, cross-over study was conducted in 67 emergency nurses. Each participant was evaluated both after completing a NS and during a day shift following a full night of rest at home (NR), with the order of assessments randomly assigned and separated by a 14-day washout period. Regional cerebral oxygenation (rSO₂) and prefrontal cortex reactivity were measured using near-infrared spectroscopy (NIRS) while performing a verbal fluency task. Cognitive performance, sleep disturbance, and state anxiety were assessed with validated instruments: State Trait Anxiety Inventory (STAI), Insomnia Severity Index, and NIRS with Verbal Fluency Test (VFT).

Results: After NS, nurses showed a significant decline in rSO₂ (67.65 ± 6.37 vs. 64.45 ± 6.78; p < 0.001), reduced performance in the VFT (47.19 ± 11.14 vs. 42.85 ± 11.99; p < 0.001), and increased state anxiety (14.63 ± 9.53 vs. 21.37 ± 7.67; p < 0.001). Shorter sleep duration during NS was associated with lower rSO₂ mean in both conditions: NR and NS. Interestingly, nurses who reported longer sleep during NS exhibited higher rSO₂ but paradoxically poorer cognitive performance, suggesting that those with shorter sleep may have engaged compensatory mechanisms in those with shorter sleep that allowed them to maintain task performance despite reduced rSO₂.

Conclusions: Reduced sleep during NS negatively affected cerebral oxygenation, cognitive performance, and emotional well-being in emergency nurses. Longer self-reported sleep did not necessarily confer cognitive benefits and may reflect inefficient or overestimated sleep.

Implications for nursing management: Organizational strategies are needed to mitigate the adverse effects of NS work. Optimizing shift schedules, ensuring structured rest opportunities, and implementing programs aimed at reducing anxiety and supporting psychological well-being could help protect nurses' health, sustain performance, and improve patient safety.