Nursing Workforce Management Research Articles

Unravelling the ideal roster: A cross-sectional study of nurse shift preferences using multivariate analysis: Unravelling the ideal roster: An analysis of nurse shift preferences.

Our study aims to explore nurses' shift preferences in relation to their personal characteristics and examine how these preferences align with the rosters imposed in Belgian healthcare settings. Additionally, the study seeks to identify patterns in shift preferences across different days of the week and investigate the existence of distinct groups of nurses with similar preferences, further examining the link between these groups and their personal characteristics. Cross-sectional. Questionnaires were distributed to 778 nurses across 11 general hospitals in Belgium, collecting data on demographics, chronotype, shift preferences, and roster alignment. Statistical analyses included logistic regression, principal component analysis, and k-means clustering. Age and chronotype significantly influence nurses' shift preferences. Preferences were consistent across the days within the week. The study revealed two groups of preferences: 'early birds' (preferring morning/day shifts) and 'night owls' (preferring evening/night shifts). Night owls were often neutral or evening-type chronotypes and had a higher alignment between imposed and ideal rosters. This study reinforces the importance of considering individual differences in nurses' shift preferences, linked to age and chronotype, and advocates for the adoption of flexible, personalized rostering systems. Personalized scheduling has the potential to improve workforce management, suggesting that healthcare administrators should consider individual preferences in rostering to mitigate the challenges of nurse understaffing. Tackles the pressing problem of nurse understaffing. Proposes that tailored rosters based on individual preferences could improve work conditions for nurses. Relevant to policymakers aiming to enhance nursing workforce management. STROBE Statement (for cross-sectional studies). None.

Innovations in Nursing Workforce Management: Integrating Emerging Technologies With Proven Strategies.

Addressing the current and future nursing workforce shortage is the most pressing topic on healthcare leaders' agenda. The pandemic worsened a looming problem, driven both by supply and demand. Classic strategies deployed in past shortages are useful but are not sufficient to guide scarce resource management in the future. Emerging innovative technology solutions, carefully selected and evaluated, can supplement and enhance past strategies.

The journey of nurse managers in the nursing workforce management during the Covid-19 pandemic

The journey of nurse managers in the nursing workforce management during the Covid-19 pandemic

Exploring nursing managers\u2019 perceptions of nursing workforce management during the outbreak of COVID-19: a content analysis study

BackgroundThe COVID-19 pandemic is a serious threat to public health worldwide. Therefore, a coordinated global response is needed to prepare health care systems to face this unprecedented challenge. Proper human resource management can increase nurses’ productivity and quality of care. Therefore, the present study aims to explore the nursing managers’ perception of nursing workforce management during the outbreak of COVID-19.MethodsThis is a qualitative study with conventional content analysis using Granheim and Landman approach. In this study, 15 nursing managers were selected by purposeful sampling method. Data were collected using in-depth semi-structured interviews. Ethical considerations were applied to all stages of the study. In this study, MAXQDA software version 10 was used to help manage the data.Results66% of the participants (10/5) were female. The mean age of participants was 44 years, mean work experience of 19 years, and mean management experience of 9 years. Three categories and seven sub-categories emerged from the data analysis: 1) management of workforce recruitment (volunteer workforces, non-volunteer workforces), 2) management of workforce arrangement (flexible work schedule, rearrangement of the workforce), and 3) management of workforce retention (preventive measures, motivational measures, and psychological support).ConclusionManagement in critical situations requires the use of flexible and situational management principles to recruit, arrange and retain workforce, and also to compensate for the lack of manpower.

Controlling understaffing with conditional Value-at-Risk constraint for an integrated nurse scheduling problem under patient demand uncertainty

Nursing workforce management is a challenging decision-making task in hospitals. The decisions are made across different timescales and levels from strategic long-term staffing budget to mid-term scheduling. These decisions are interconnected and impact each other, therefore are best taken by considering staffing and scheduling together. Moreover, this decision-making needs to be made in a stochastic setting to meet uncertain patient demand. A sufficient and cost-efficient staffing level with desirable schedule is essential to provide good working conditions for nurses and consequently good quality of care. On the other hand, understaffing can severely deteriorate the quality of care thus should be strictly controlled.To help with the decision making, based on our previous research we formulate in this paper an integrated nurse staffing and scheduling model under patient demand uncertainty into a two-stage stochastic programming model with an emphasis on understaffing risk control. Conditional Value-at-Risk (CVaR), a risk control measure primarily used in the financial domain, is integrated in the stochastic programming model to control understaffing risk. The IBM ILOG CPLEX solver is applied to solve the stochastic model. The model and solution approaches are tested using a case study in a real-world environment setting. We have evaluated the performance of the stochastic model and the benefit of CVaR in terms of impact on schedule quality.

Global Nursing-a literature review in the field of education and practice.

AimTo describe key findings of Global Nursing in empirical nursing studies.DesignA literature review using descriptive data synthesis of peer‐reviewed articles in the field of nursing education and practice.MethodsThis review of Cinahl Complete, PubMed, PsycINFO and Scopus was undertaken using the search strategy “global nursing”. Independent title/abstract and full‐text screening was undertaken, identifying original articles written in English.ResultsA total of 472 titles and 170 abstracts were read through. Seventy‐three articles were included for full‐text review. Twenty published studies of Global Nursing with multiple research methodologies fulfilled the inclusion criteria. Findings were described with five categories. Global Nursing Arena, Global Nursing Working Environments, Global Nursing Workforce Management, Global Nursing Competencies and Global Nursing Networking were shown to be crucial when Global Nursing was addressed in the literature.

Hospital mortality and optimality of nursing workload: A study on the predictive validity of the RAFAELA Nursing Intensity and Staffing system

BackgroundPatient classification systems have been developed to manage workloads by estimating the need for nursing resources through the identification and quantification of individual patients’ care needs. There is in use a diverse variety of patient classification systems. Most of them lack validity and reliability testing and evidence of the relationship to nursing outcomes. ObjectivePredictive validity of the RAFAELA system was tested by examining whether hospital mortality can be predicted by the optimality of nursing workload. MethodsIn this cross-sectional retrospective observational study, monthly mortality statistics and reports of daily registrations from the RAFAELA system were gathered from 34 inpatient units of two acute care hospitals in 2012 and 2013 (n=732). The association of hospital mortality with the chosen predictors (hospital, average daily patient to nurse ratio, average daily nursing workload and average daily workload optimality) was examined by negative binomial regression analyses. ResultsCompared to the incidence rate of death in the months of overstaffing when average daily nursing workload was below the optimal level, the incidence rate was nearly fivefold when average daily nursing workload was at the optimal level (IRR 4.79, 95% CI 1.57–14.67, p=0.006) and 13-fold in the months of understaffing when average daily nursing workload was above the optimal level (IRR 12.97, 95% CI 2.86–58.88, p=0.001). ConclusionsHospital mortality can be predicted by the RAFAELA system. This study rendered additional confirmation for the predictive validity of this patient classification system. In future, larger studies with a wider variety of nurse sensitive outcomes and multiple risk adjustments are needed. Future research should also focus on other important criteria for an adequate nursing workforce management tool such as simplicity, efficiency and acceptability.

종합병원 간호사의 조직시민행동 영향요인

Objectives : Identify the factors that affect organizational citizenship behavior of hospital nurses, and to provide a basis for developing an effective nursing workforce management system. Methods : Participants of this study were 214 nurses serving at general hospitals located in B Metropolitan City. Data collection was performed from Oct.15th to 25th, 2014. The methods of analyzing data were ANOVA, Scheffe`s-Test, Pearson`s Correlation Coefficient Analysis and the Stepwise Multiple Regression Analysis according to the SPSS WIN 18.0 Program. Results : Emotional intelligence explained 42.1% of the subjects` organizational citizenship behaviors. This figure increased to 48.7, 51.3 and 52.3%, respectively when other three factors of job satisfaction, age and educational background were added on a one-by-one basis. Conclusions : Considering that emotional intelligence is a critical factor in organizational citizenship behaviors, general hospitals should develop and implement education and training programs that can raise their nurses` emotional intelligence and job satisfaction and, concomitantly, promote their organizational citizenship behaviors provided that feedbacks is provided later.

Enhancing Nursing Staffing Forecasting With Safety Stock Over Lead Time Modeling

In balancing competing priorities, it is essential that nursing staffing provide enough nurses to safely and effectively care for the patients. Mathematical models to predict optimal "safety stocks" have been routine in supply chain management for many years but have up to now not been applied in nursing workforce management. There are various aspects that exhibit similarities between the 2 disciplines, such as an evolving demand forecast according to acuity and the fact that provisioning "stock" to meet demand in a future period has nonzero variable lead time. Under assumptions about the forecasts (eg, the demand process is well fit as an autoregressive process) and about the labor supply process (≥1 shifts' lead time), we show that safety stock over lead time for such systems is effectively equivalent to the corresponding well-studied problem for systems with stationary demand bounds and base stock policies. Hence, we can apply existing models from supply chain analytics to find the optimal safety levels of nurse staffing. We use a case study with real data to demonstrate that there are significant benefits from the inclusion of the forecast process when determining the optimal safety stocks.

Vacant hospitals and under-employed nurses: a qualitative study of the nursing workforce management situation in Nepal

It is vital for all healthcare systems to have a sufficient number of suitably trained health professionals including nurses at all levels of health services to deliver effective healthcare. An ethnographic, qualitative method was chosen for this study, which included open-ended, in-depth interviews with a range of stakeholders including student nurses, qualified nurses, nurse managers and lecturers, and the human resource co-ordinator in the Ministry of Health and Population. Available records and policy documents were also analysed. Study findings suggest that there is a severe mal-distribution of the nursing workforce in rural and urban healthcare centres in Nepal. Although there is an oversupply of newly qualified nurses in hospitals in Kathmandu, the staffing situation outside the valley is undesirable. Additionally, the turnover of junior nursing staff remains high in major urban hospitals. Most qualified nurses aspire to work in developed countries, such as the UK, North America, Australia and New Zealand. Between 2000 and 2008, as many as 3000 nurses have left Nepal for jobs in the developed west. There is no effective management strategy in place to retain a nursing workforce, particularly in rural Nepal. This article concludes by proposing some suggestions for a nursing workforce retention policy to address this critical issue.

Reexamining the Nurse Scheduling Problem: Staffing Ratios and Nursing Shortages*

ABSTRACTLegislators at the state and national levels are addressing renewed concerns over the adequacy of hospital nurse staffing to provide quality care and ensure patient safety. At the same time, the well‐known nursing shortage remains an ongoing problem. To address these issues, we reexamine the nurse scheduling problem and consider how recent health care legislation impacts nursing workforce management decisions. Specifically, we develop a scheduling model and perform computational experiments to evaluate how mandatory nurse‐to‐patient ratios and other policies impact schedule cost and schedule desirability (from the nurses' perspective). Our primary findings include the following: (i) nurse wage costs can be highly nonlinear with respect to changes in mandatory nurse‐to‐patient ratios of the type being considered by legislators; (ii) the number of undesirable shifts can be substantially reduced without incurring additional wage cost; (iii) more desirable scheduling policies, such as assigning fewer weekends to each nurse, have only a small impact on wage cost; and (iv) complex policy statements involving both single‐period and multiperiod service levels can sometimes be relaxed while still obtaining good schedules that satisfy the nurse‐to‐patient ratio requirements. The findings in this article suggest that new directions for future nurse scheduling models, as it is likely that nurse‐to‐patient ratios and nursing shortages will remain a challenge for health care organizations for some time.

Nursing Workforce Management

The goal of maintaining the balance between demand for nursing care and the supply of nurses is the basis for state and regional planning to meet future nursing workforce needs. Projecting nursing supply needs solely on the basis of historical registered nurse to population ratios does not specifically consider the healthcare needs of the population. The authors present a model in which state population data and nursing resource data are compared with national data to assist state planning groups in developing an effective workforce management plan. Integrating population demographic data, healthcare needs of citizens, and nurse resource factors at the state level can improve the accuracy of projection statistics.