How Does Servant Leadership Nurture Nurses' Job Embeddedness? Uncovering Sequential Mediation of Psychological Contract Fulfillment and Psychological Ownership

Aim. This research aimed to explore how servant leadership nurtures nurses' job embeddedness by uncovering the sequential mediation of psychological contract fulfillment and psychological ownership. Background. The healthcare of Pakistan is undergoing an acute shortage of 1.3 million nurses. The gap is widening due to unprecedented natural uncertainties (floods, earthquakes, COVID-19, dengue, polio, and monkeypox) and the large-scale brain drain of nurses. Therefore, exploring the underlying factors that could facilitate nurses' job embeddedness is imperative. Methods. A cross-sectional research design was employed, wherein data were gathered in three rounds, two months apart, from 587 nurses employed in public hospitals in Pakistan, and analysis was performed with Smart-PLS. Results. Servant leadership positively influences nurses' job embeddedness and psychological contract fulfillment. Besides, psychological contract fulfillment positively affects psychological ownership, and psychological ownership enhances nurses' job embeddedness. Finally, psychological contract fulfillment and psychological ownership sequentially mediate the relationship between servant leadership and job embeddedness. Conclusions. This research emphasized the vitality of servant leadership in nurturing nurses' job embeddedness. Implications for Nursing Management. Healthcare authorities should keenly focus on promoting servant leadership that shapes the positive perception of nurses about their psychological contract fulfillment and psychological ownership, which are essential resources to cherish nurses' job embeddedness.

The interplay between servant leadership, psychological safety, trust in a leader and burnout: assessing causal relationships through a three-wave longitudinal study.

COVID-19 pandemic has brought unprecedented psychological challenges for frontline healthcare workers, especially nurses, causing anxiety and depression leading to burnout. The responsibility of healthcare leaders has increased manyfold to deal with such challenges. This study attempts to employ the conservation of resources theory to examine the relationship between servant leadership and nurses' burnout, with the mediating role of psychological safety and the moderating effect of trust in leader. A three-wave longitudinal design was employed for data collection from 1204 nurses from 27 hospitals in China. The partial least squares structural equation modeling technique was used for data analyses with SmartPLS version 3.2.8. The findings endorse that servant leadership at time 1 significantly reduces nurses' burnout measured at time 3 through the mediating role of psychological safety measured at time 2, and that a higher level of trust in the leader enhances the impact of servant leadership in reducing nurses' burnout.

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques.

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.

Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset.

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

Curbing nurses' burnout during COVID-19: The roles of servant leadership and psychological safety.

AIMS: This study examines the role of servant leadership through the mechanism of psychological safety in curbing nurses' burnout during the COVID-19 pandemic. BACKGROUND: During the COVID-19 pandemic, studies have shown an increased level of stress and burnout among health care workers, especially nurses. This study responds to the call for research to explore the mechanisms of servant leadership in predicting nurses' burnout by employing the perspective of conservation of resources theory. METHODS: Through a cross-sectional quantitative research design, data were collected in three waves from 443 nurses working in Pakistan's five public sector hospitals. Data were analysed by employing the partial least squares path modelling (PLS-PM) technique. RESULTS: Servant leadership (ß = -0.318; 95% CI = 0.225, 0.416) and psychological safety (ß = -0.342; CI = 0.143, 0.350) have an inverse relationship with nurses' burnout and explain 63.1% variance. CONCLUSIONS: Servant leadership significantly reduces nurses' burnout, and psychological safety mediates this relationship. IMPLICATIONS FOR NURSING MANAGEMENT: Human resource management policies in health care must emphasize training nursing leaders in servant leadership behaviour.

How inclusive leadership paves way for psychological well-being of employees during trauma and crisis: A three-wave longitudinal mediation study.

AIMS: Nurses are at the forefront of public health emergencies facing psychological pressures ensuing from the loss of patients and potential risk of infection while treating the infected. This study examines whether inclusive leadership has a causal relationship with psychological distress and to assess the mediation effect of psychological safety on this relationship in the long run. The hypotheses are developed and interpreted with the help of theoretical underpinnings from job demands resources theory and the theory of shattered assumptions. DESIGN: Three-wave longitudinal study. METHODS: Questionnaire was used to carry out three waves of data collection from 405 nurses employed at five hospitals in Wuhan during the COVID-19 outbreak between the months of January-April 2020. Partial least square structural equation modelling (PLS-SEM) was used to analyze data while controlling for age, gender, education, experience, and working hours. RESULTS: Results supported the hypothesized relationships where inclusive leadership indicated significant inverse causal relationship with psychological distress and a positive causal relationship with psychological safety. Mediation effect of psychological safety was found significant, while the model explained 73.9% variance in psychological distress. CONCLUSION: Inclusive leadership, through its positive and supportive characteristics, can pave way for such mechanisms that improve the psychological safety of employees in the long run and curbs psychological distress. IMPACT: This is the first longitudinal study to examine the relationship between inclusive leadership and psychological distress in health care and also examines the mediating mechanism of psychology safety. There is scarcity of empirical research on factors that determine and affect behavioural mechanism of healthcare workers during traumatic events and crisis. Clinical leaders and healthcare policy makers must invest in and promote inclusive and supportive environment characterized with open and accessible leaders at workplace to improve psychological safety; it helps reduce levels of psychological distress.

How and When Does Inclusive Leadership Curb Psychological Distress During a Crisis? Evidence From the COVID-19 Outbreak.

Traumatic events such as a pandemic shatter the assumption of the workplace as a safe place. Nurses face risks of life-threatening infection, which can create psychological distress. Quality of care for infected patients depends on mental well-being of nurses which calls for research on predictors of stress among health care workers. Responding to a call for research on the effects of leadership styles on psychological distress during traumatic events, this paper uses the theoretical lens of social exchange theory and contributes to literature on relationships between inclusive leadership, psychological distress, work engagement, and self-sacrifice. Participants of this cross sectional study included 497 registered nurses from five hospitals in Wuhan. Data were collected with temporal separation through an online questionnaire. Partial least-squares structural equation modeling was used to analyze data. Results show inclusive leadership has a significant negative relationship with psychological distress. Work engagement mediates this relationship, and nurses' self-sacrificial behavior moderates it. Findings indicate inclusive leadership style serves as a sustainable mechanism to reduce psychological distress during pandemics. It can operationalize the delivery of mental health support in real-time in work settings. Results provide empirical support for social exchange theory through high work engagement to help control psychological distress among nurses.

Caring for the caregiver during COVID-19 outbreak: Does inclusive leadership improve psychological safety and curb psychological distress? A cross-sectional study.

BACKGROUND: Public health emergencies and epidemics shatter the assumptions of the world as a safe place. Healthcare workers are at the forefront of such pressures resulting from a persistent threat to their safety and well being. It is therefore important to study such mechanisms that can influence and predict the psychological distress of nurses OBJECTIVES: While there is an increasing number of studies on positive outcomes of leadership styles, their influence on curbing unwanted adverse outcomes is scarce. This study aims to observe the influence of an inclusive leadership style on psychological distress while assessing the mediating role of psychological safety. It uses the theoretical lens of job demands-resources theory and the theory of shattered assumptions to develop and test hypotheses. DESIGN: Cross-Sectional Study with Temporal Separation SETTINGS AND PARTICIPANTS: The researchers recruited 451 on-duty registered nurses from 5 hospitals providing patient care during the highly infectious phase of COVID-19 in January 2020 in Wuhan city, the epicentre of the outbreak in China METHODS: After obtaining permission from hospital administration, data were collected through an online questionnaire survey in three stages with temporal separation to avoid common method bias. Partial least square structural equation modelling was used to analyze data. The study controlled for effects of age, gender, experience, working hours and education. RESULTS: Hypothesized relationships proved significant. Inclusive leadership has an inverse relationship with psychological distress with a strong path-coefficient. Psychological safety mediates the relationship between inclusive leadership and psychological distress while explaining 28.6% variance. Multi-group analysis results indicate no significant differences between respondents based on these control variables CONCLUSIONS: Recurring or prolonged experiences of stress and anxiety at the workplace, without a mechanism to counter such effects, can culminate into psychological distress. Inclusive leadership style can serve as such a mechanism to curb psychological distress for healthcare workers by creating a psychologically safe environment.