How robots impact nurses' time pressure and turnover intention: A two-wave study

The impact of digital technology use on nurses' professional identity and relations of power: a literature review

AIM

This study seeks to review how the use of digital technologies in clinical nursing affects nurses' professional identity and the relations of power within clinical environments.

DESIGN

Literature review.

DATA SOURCES

PubMed and CINAHL databases were searched in April 2023.

METHODS

We screened 874 studies in English and German, of which 15 were included in our final synthesis reflecting the scientific discourse from 1992 until 2023.

RESULTS

Our review revealed relevant effects of digital technologies on nurses' professional identity and power relations. Few studies cover outcomes relating to identity, such as moral agency or nurses' autonomy. Most studies describe negative impacts of technology on professional identity, for example, creating a barrier between nurses and patients leading to decreased empathetic interaction. Regarding power relations, technologically skilled nurses can yield power over colleagues and patients, while depending on technology. The investigation of these effects is underrepresented.

CONCLUSION

Our review presents insights into the relation between technology and nurses' professional identity and prevalent power relations. For future studies, dedicated and critical investigations of digital technologies' impact on the formation of professional identity in nursing are required.

IMPLICATIONS FOR THE PROFESSION

Nurses' professional identity may be altered by digital technologies used in clinical care. Nurses, who are aware of the potential effects of digitized work environments, can reflect on the relationship of technology and the nursing profession.

IMPACT

The use of digital technology might lead to a decrease in nurses' moral agency and competence to shape patient-centred care. Digital technologies seem to become an essential measure for nurses to wield power over patients and colleagues, whilst being a control mechanism. Our work encourages nurses to actively shape digital care.

REPORTING METHOD

We adhere to the JBI Manual for Evidence Synthesis where applicable. EQUATOR reporting guidelines were not applicable for this type of review.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Missed Nursing Care as a Mediator in the Relationship between Career Calling and Turnover Intention

AIM

To explore the role of missed nursing care in mediating the relationship between career calling and intention to leave among nurses.

INTRODUCTION

Increasing nurse turnover is still a major concern in the global healthcare system. The most reliable indicator of turnover is turnover intention. It is crucial to understand its affecting elements to suggest measures to lower nurses' turnover intention.

BACKGROUND

Turnover intention has been linked to career calling and missed nursing care. Little empirical research has investigated the possibility that missed nursing care mediates between career calling and turnover intention.

METHODS

A cross-sectional survey of 347 nurses was conducted. The survey instruments included the General Information Questionnaire, Calling Scale, Missed Nursing Care Scale and Turnover Intention Questionnaire. Structural equation models were used to build the model. This study made use of the STROBE checklist.

RESULTS

For 43.8% of nurses, turnover intention was high or very high. Missed nursing care and turnover intention were negatively correlated with career calling. Missed nursing care and turnover intention were positively related. Missed nursing care mediated the relationship between career calling and turnover intention.

DISCUSSION

Career calling and missed nursing care can both influence turnover intention. Career calling can reduce the likelihood of turnover by preventing missed nursing care.

CONCLUSION

Missed nursing care mediated the relationship between career calling and intention to leave.

IMPLICATIONS FOR NURSING AND NURSING POLICY

Nursing managers should improve nurses' career calling through professional education and minimize missed nursing care by using electronic nursing reminder devices to reduce turnover intention.

Estimating the number of required nurses in different types of hospitals: An application of the workload indicators of staffing needs (WISNS) method

BACKGROUND

Health system performance depends on the availability, accessibility, acceptability, and quality of health workforces. Policymakers seek whether the number of nurses is optimally matched based on patients' needs. This study aimed to assess the workforce stock, workload activities, activity standards, and workload pressure to determine the number of required nurses in different types of hospitals in Iran.

METHODS

This study applied the workload indicators of staffing needs (WISNs) method and was conducted in 22 surgical and internal medicine wards at five hospitals in the southwest of Iran during six months. A time-motion study, and several group discussions, interviews were used to extract the required data. Descriptive statistics were used for data analysis.

RESULTS

All selected hospitals faced nursing shortages. The highest shortage (-47) and workload pressure (WISN ratio 0.45) were observed in the general-educational hospitals. In the specialized hospitals, the workload pressure was high (WISN ratio 0.49). The lowest shortage belonged to the private hospital. Based on our assessment, in all of the hospitals, nurses typically worked overtime due to high workload. The studied hospitals covered an average of 25% of their shortage with nursing overtime working. We noted that nurses were predominantly occupied with health service and supportive activities (≈90% of their time).

CONCLUSIONS

Based on the WISN method, all of the hospitals faced nursing shortages from moderate to high. However, it would be essential to consider current labor market analysis based on accurate data to adopt appropriate policies in HRH planning.

Retaining Healthcare Workers: A Systematic Review of Strategies for Sustaining Power in the Workplace

The shortage of healthcare workers is a growing concern. The COVID-19 pandemic and retirement wave have accelerated turnover rates. This systematic review aimed to identify and analyse the existing interventions for job retention of healthcare workers, in terms of nurses and physicians, in a hospital setting. A comprehensive search was conducted within three electronic databases, guided by the preferred reporting items for systematic review and meta-analyses (PRISMA) and synthesis without meta-analysis (SWiM) guidelines, this resulted in 55 records that met the inclusion criteria. The intervention outcomes are categorized into substantial themes: onboarding, transition program to a different unit, stress coping, social support, extra staffing, coping with the demands of patient care, work relationships, development opportunities and department resources, job environment, work organization, recruitment approach, and technological innovations. Considering the literature, onboarding programs and mentoring for nurses and physicians are recommended. Additionally, other interventions described in this review could positively affect the retention of nurses and physicians. When selecting an intervention for implementation, managers and human resources should consider the intervention that matches the determinant of intention to leave of their healthcare workers and the hospital's mission, vision, and values. Sharing the success stories of implemented interventions may benefit healthcare organizations.

Artificial intelligence-based intelligent surveillance for reducing nurses' working hours in nurse-patient interaction: A two-wave study

AIM

To explore the feasibility of applying artificial intelligence in nurse-patient interaction to assist nurses in grasping patient status and reducing their working hours.

BACKGROUND

Artificial intelligence has been reshaping the health care industry and has immense potential in nursing care, but there is still a lack of suitable artificial intelligence methods to improve the efficiency of the nurse-patient interaction that takes much time of nurses.

METHODS

An artificial intelligence-based intelligent surveillance system was developed to reduce nurses' working hours in nurse-patient interaction, and a two-wave follow-up design was adopted in this study. The data were collected in a nursing home in Guangzhou, China. The first and second waves of data were recorded in the same format on the same patients by the same nurses. The only difference is the deployment of artificial intelligence technology between the two waves of data.

RESULTS

Artificial intelligence-based intelligent surveillance can provide statistical health data for nurses to grasp the patients' status more intuitively, reducing the average nurse-patient interaction time per patient from 18 to 10 min. In addition, artificial intelligence's real-time response to the abnormal health status of patients not only avoids more serious secondary injuries for patients but also prevents nurses from consuming energy in detecting emergencies.

CONCLUSION

The application of artificial intelligence has great potential to reduce nurses' working hours in nurse-patient interaction. There are still many limitations in artificial intelligence technology at this stage, and it is not feasible to completely rely on artificial intelligence. However, as a tool to assist decision-making, it can still have beneficial impacts on nursing management.

IMPLICATIONS FOR NURSING MANAGEMENT

Artificial intelligence has great potential in daily nurse-patient interaction, and nursing facility managers and nurses need to be more open to this new technology.