Nursing shortage in the public healthcare system: an exploratory study of Hong Kong

The role of psychological flexibility in the relationships between burnout, job satisfaction, and mental health among nurses in combatting COVID-19: A two-region survey

BACKGROUND

A growing body of evidence suggests that the COVID-19 pandemic is adversely impacting the mental health and well-being of frontline nurses worldwide. It is therefore important to understand how such impact can be mitigated, including by studying psychological capacities that could help the nurses regulate and minimize the impact.

AIM

To examine the role of psychological flexibility in mitigating the adverse impacts of burnout and low job satisfaction on mental health problems (i.e., anxiety, depression, and stress) and well-being among the frontline nurses in Hong Kong and Switzerland during the COVID-19 pandemic.

DESIGN

Cross-sectional, two-region survey study.

METHOD

Four hundred fifty-two nurses from Hong Kong (n = 158) and Switzerland (n = 294) completed an online survey. An adjusted structured equation model was used to examine the interrelationship of the constructs.

RESULTS

Psychological flexibility was found to partially mediate the effects of job satisfaction on mental well-being (β = 0.32, 95% CI [0.19, 0.57], p = 0.001) and mental health problems (β = -0.79, 95% CI [-1.57, -0.44], p = 0.001), respectively. Similarly, this partial mediation was found in the effects of burnout on mental well-being (β = -0.35, 95% CI [-0.89, -0.15], p = 0.002) and mental health problems (β = 0.89, 95% CI [0.48, 3.65], p = 0.001).

CONCLUSION

Psychological flexibility could be a crucial psychological resilience factor against the adverse impact of nurses' burnout on their mental health problems and well-being during COVID-19.

CLINICAL RELEVANCE

Organizational measures should focus on fostering psychological flexibility in nurses through highly accessible, brief psychotherapeutic interventions, such as Acceptance and Commitment Therapy, to reduce the impact on mental health.

A systematic review study on the factors affecting shortage of nursing workforce in the hospitals

AIM

This study aimed to determine factors that influence the nursing workforce shortage and their impact on nurses.

DESIGN

This study applied a systematic review design.

METHODS

Using Cochrane library guidelines, five electronic databases were systematically searched (Research 4life-PubMed/Medline, Scopus, Embase, CINAHL) from 2010-2021. The remaining articles with pertinent information were presented in a data extraction sheet for further thematic analysis. A Reporting Items for Systematic Reviews and Meta-Analysis Flow Diagram was adopted and used. The studies published from 2010-2021 and in English language were examined and included in the systematic review.

RESULTS

Four themes were identified as factors influencing the nursing workforce shortage, including Policy and planning barriers, Barriers to training and enrolment, Factors causing nursing staff turnover and Nurses' stress and burnout. Nursing workforce shortage is a global challenge that roots in multiple causes such as individual, educational, organizational and managerial and policy-making factors.

Is there a gap between artificial intelligence applications and priorities in health care and nursing management?

AIM

The article aims to outline a contrast between three priorities for nursing management proposed a decade ago and key features of the following 10 years of developments on artificial intelligence for health care and nursing management. This analysis intends to contribute to update the international debate on bridging the essence of health care and nursing management priorities and the focus of artificial intelligence developers.

BACKGROUND

Artificial intelligence research promises innovative approaches to supporting nurses' clinical decision-making and to conduct tasks not related to patient interaction, including administrative activities and patient records. Yet, even though there has been an increase in international research and development of artificial intelligence applications for nursing care during the past 10 years, it is unclear to what extent the priorities of nursing management have been embedded in the devised artificial intelligence solutions.

EVALUATION

Starting from three priorities for nursing management identified in 2011 in a special issue of the Journal Nursing Management, we went on to identify recent evidence concerning 10 years of artificial intelligence applications developed to support health care management and nursing activities since then.

KEY ISSUE

The article discusses to what extent priorities in health care and nursing management may have to be revised while adopting artificial intelligence applications or, alternatively, to what extent the direction of artificial intelligence developments may need to be revised to contribute to long acknowledged priorities of nursing management.

CONCLUSION

We have identified a conceptual gap between both sets of ideas and provide a discussion on the need to bridge that gap, while admitting that there may have been recent field developments still unreported in scientific literature.

IMPLICATIONS FOR NURSING MANAGEMENT

Artificial intelligence developers and health care nursing managers need to be more engaged in coordinating the future development of artificial intelligence applications with a renewed set of nursing management priorities.

Related factors influencing Chinese psychiatric nurses' turnover: A cross-sectional study

WHAT IS KNOWN ON THE SUBJECT

Because of increasingly stressful, dangerous and unpredictable psychiatric nursing work, psychiatric nurses have experienced higher job stress than general ward nurses. Little is known about the factors that affect the turnover intention of Chinese psychiatric nurses. Understanding the influencing factors of nurses' turnover intention will help to formulate targeted measures to stabilize psychiatric nursing teams.

WHAT DOES THIS PAPER ADD TO EXISTING KNOWLEDGE

The results showed that 70.2% of psychiatric nurses had higher turnover intention. The strong turnover intention of Chinese psychiatric nurses is a problem that needs to be considered by managers. The results showed that having more children, between 31 and 39 years old, and having a part-time job were strongly associated with turnover intention. In addition, "job stress" was also an important factor, psychiatric nurses' turnover intention decreased as their job stress level decreased.

WHAT ARE THE IMPLICATIONS FOR PRACTICE

Nursing managers should pay attention to nurses who have more children, between 31 and 39 years old, and take on part-time jobs. Additionally, nursing managers should reduce job stress and implement targeted programmes to prevent psychiatric nurses' turnover. Experience-sharing meetings and mindfulness-based stress reduction training are also useful to improve the mental health status of psychiatric nurses with great job stress. Nursing managers should arrange human resources and shifts appropriately to give nurses with more children more time with their families. Provide more development opportunities for psychiatric nurses between 31 and 39 years old. Managers explore the reasons why nurses take on part-time jobs and take targeted interventions (such as increasing income) to reduce the behaviour that happens.

ABSTRACT

Introduction Nurses' turnover is the main cause of nursing shortages, greatly affected by nurses' intention to leave. Nurses' turnover rate is particularly high in psychiatric wards. Several factors influencing the turnover intention of psychiatric nurses have not been well identified in China, and the association between job stress and turnover intention is still limited. Aims To examine the relationship between job stress and turnover intention and identify the influencing factors of psychiatric nurses' turnover intention. Methods Data were collected from 2355 psychiatric Chinese nurses using a cross-sectional design with an online questionnaire investigation. Results Psychiatric nurses had higher turnover intention. Significant factors influencing their turnover intention were job stress, having more children, age between 31 and 39 years old, part-time jobs, education, income and patient-to-nurse ratio. Discussion Demographics and job-related factors should be considered when developing strategies to reduce the turnover intention of psychiatric nurses. Implications for practice Nursing managers should pay attention to nurses with higher job stress levels and different demographic characteristics. Effective measures should be taken to reduce psychiatric nurses' job stress and turnover intention, such as arranging reasonable shifts, implementing targeted family-friendly policies, increasing their occupational possibilities and promoting mental health.

Application of a Hybrid Multi-Criterion Decision-Making Model for Evaluation and Improvement of Nurses' Job Satisfaction

Background

The global shortage and turnover of nurses is a current challenge. Past studies have shown that nurse job satisfaction may ameliorate nurse shortage. Although there are many studies on the criteria influencing nurses' job satisfaction, few have examined the causal relationships and weight of each criterion from a systematic perspective.

Objective

Identify the key criteria and causal relationships that affect nurses' job satisfaction, and help nurse leaders identify high-weight, high-impact dimensions and contextualize them for improvement.

Methods

The study developed a hybrid multi-criterion decision-making model, which incorporated the McCloskey/Mueller satisfaction 13-item scale (MMSS-13), and the Decision-Making Trial and Evaluation Laboratory and the Importance-Performance Analysis methods the model was used to analyze key factors of nurse satisfaction and their interrelationships based on the experience of 15 clinical nurse specialists.

Results

In MMSS-13's dimension level, “satisfaction with work conditions and supervisor support” (C₅) had the highest impact, and “satisfaction with salary and benefits” (C₁) had the highest weight. In criteria level, “salary” (C₁₁), “flexibility in scheduling time off” (C₂₄), “maternity leave time” (C₃₁), “opportunities for social contact after work” (C₄₁), and “your head nurse or facility manager” (C₅₁) had high influence under their corresponding dimensions. The “benefits package” (C₁₃) was the top criterion with the highest impact on MMSS-13.

Conclusions

This study assessed nurses' job satisfaction from a multidimensional perspective and revealed the causal relationships between the dimensions. It refined the assessment of nurse job satisfaction to help nurse leaders better assess nurse job satisfaction and make strategic improvements. The study found that compensation and benefits had the highest weight in nurses' job satisfaction. Meanwhile, support for family responsibilities and working conditions, and support from supervisors were the cause dimensions of job satisfaction. Among the more detailed criteria, salary, benefits package, maternity leave time, and leadership had a greater impact on nurses' job satisfaction. Nurse leaders should start with these dimensions to achieve efficient improvement of nurses' job satisfaction.

A Two-Echelon Responsive Health Analytic Model for Triggering Care Plan Revision in Geriatric Care Management

Due to the increasing ageing population, how can caregivers effectively provide long-term care services to meet the older adults’ needs with finite resources is emerging. In addressing this issue, nursing homes are striving to adopt smart health with the internet of things and artificial intelligence to improve the efficiency and sustainability of healthcare. This study proposed a two-echelon responsive health analytic model (EHAM) to deliver appropriate healthcare services in nursing homes under the Internet of Medical Things environment. A novel care plan revision index is developed using a dual fuzzy logic approach for multidimensional health assessments, followed by care plan modification using case-based reasoning. The findings reveal that EHAM can generate patient-centred long-term care solutions of high quality to maximise the satisfaction of nursing home residents and their families. Ultimately, sustainable healthcare services can be within the communities.

Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes

Due to the global ageing population, the increasing demand for long-term care services for the elderly has directed considerable attention towards the renovation of nursing homes. Although nursing homes play an essential role within residential elderly care, professional shortages have created serious pressure on the elderly service sector. Effective workforce planning is vital for improving the efficacy and workload balance of existing nursing staff in today's complex and volatile long-term care service market. Currently, there is lack of an integrated solution to monitor care services and determine the optimal nursing staffing strategy in nursing homes. This study addresses the above challenge through the formulation of nursing staffing optimisation under the blockchain-internet of things (BIoT) environment. Embedding a blockchain into IoT establishes the long-term care platform for the elderly and care workers, thereby decentralising long-term care information in the nursing home network to achieve effective care service monitoring. Moreover, such information is further utilised to optimise nursing staffing by using a genetic algorithm. A case study of a Hong Kong nursing home was conducted to illustrate the effectiveness of the proposed system. We found that the total monthly staffing cost after using the proposed model was significantly lower than the existing practice with a change of −13.48%, which considers the use of heterogeneous workforce and temporary staff. Besides, the care monitoring and staffing flexibility are further enhanced, in which the concept of skill substitution is integrated in nursing staffing optimisation.

Model of Organizational Commitment Applied to Health Management Systems

In this paper, we try to build on the problems surrounding the management of human resources in health care organizations worldwide. After the analysis of the reviewed literature, we detected that the scientific community considers several recurring themes that need attention: stress, burnout, and turnover intention. Based on this, we developed a model of organizational commitment that aims to achieve performance and health quality, its main result the establishment of the appropriate management policies in order to avoid the abandonment of the organization through the search for commitment and job satisfaction. Amongst our main conclusions, we highlight the need to implement a human resources model for hospital administrators based on the relationships with "patients" not "clients" through the maintenance of a positive and strong atmosphere of staff participation. It is important to develop innovative practices related to clear job design that eliminate reasons for ambiguity and stress in executing the tasks of the healthcare system. Finally, we urge training programs in transformational leadership to promote the well-being and organizational commitment of employees.