Commuting to work: RN travel time to employment in rural and urban areas

Differences in shift and work-related patterns between metropolitan and regional/rural healthcare shift workers and the occupational health and safety risks

OBJECTIVES

To explore if there are differences in shift patterns and work-related factors between metropolitan and regional/rural healthcare shift workers and their risk of poor sleep and mental health. Furthermore, explore whether these factors impact on medical errors, workplace and car/near car accidents.

DESIGN

A cross-sectional study.

SETTING

An anonymous online survey of healthcare shift workers in Australia.

PARTICIPANTS

A total of 403 nurses, midwives and paramedics completed the survey.

MAIN OUTCOME MEASURES

Sample characteristics, employment location, shift work-related features, sleep and mental health measurements, workplace accidents, medical errors and car/near car accident post shift.

RESULTS

Regional/rural healthcare shift workers were significantly older, had more years' experience, worked more nights, on-call and hours per week. Those in metropolitan areas took significantly longer (minutes) to travel to work, had higher levels of anxiety, increased risk of shift work disorder, reported significantly more workplace accidents and were more likely to have a car/near car accident when commuting home post shift. Both groups reported ~25% having a medical error in the past year. Workplace accidents were related to more on-call shifts and poor sleep quality. Medical errors were associated with fewer years' experience, more evening shifts and increased stress. Car accidents were associated with metropolitan location and increased depression.

CONCLUSION

Differences in work-related factors between metropolitan and regional/rural healthcare shift workers were observed. Some of these factors contributed to occupational health and safety risks. Further exploration is needed to understand how to reduce occupational health and safety risks, and improve employee and patient safety both in both regional/rural and metropolitan areas.

Investigating factors that reduce burnout among newly licensed registered nurses

BACKGROUND: Burnout among newly licensed registered nurses is a prevalent healthcare issue. OBJECTIVE: To investigate the factors that reduce nurse burnout: workload, job interference with home life, having friendly co-workers, and inspirational motivation. METHODS: Secondary data from Kovner et al.’s 2006–2015 longitudinal survey was used to conduct a factor analysis. Kovner et al. surveyed 1,706 newly licensed nurses who received their nursing licenses in 2002 in the United States. The factor analysis identified the relationships between several questions about burnout symptoms and organizational factors. RESULTS: Supervisors inspiring the best in their employees, job satisfaction, and nurses’ job expectations matching their actual job duties were found to be related. Heavy workloads, nervousness, mood swings, irritability, and feeling on edge were also related. Additionally, job’s interference with home life and friendly co-workers were unrelated. CONCLUSIONS: Burnout can be mitigated by lessening workloads, supporting nurses, and ensuring nurses’ job expectations are met. Further research is needed to explain why some factors, such as having friendly co-workers, did not appear to influence burnout symptoms.

Utility of Facebook's Social Connectedness Index in Modeling COVID-19 Spread: Exponential Random Graph Modeling Study

BACKGROUND

The COVID-19 (the disease caused by the SARS-CoV-2 virus) pandemic has underscored the need for additional data, tools, and methods that can be used to combat emerging and existing public health concerns. Since March 2020, there has been substantial interest in using social media data to both understand and intervene in the pandemic. Researchers from many disciplines have recently found a relationship between COVID-19 and a new data set from Facebook called the Social Connectedness Index (SCI).

OBJECTIVE

Building off this work, we seek to use the SCI to examine how social similarity of Missouri counties could explain similarities of COVID-19 cases over time. Additionally, we aim to add to the body of literature on the utility of the SCI by using a novel modeling technique.

METHODS

In September 2020, we conducted this cross-sectional study using publicly available data to test the association between the SCI and COVID-19 spread in Missouri using exponential random graph models, which model relational data, and the outcome variable must be binary, representing the presence or absence of a relationship. In our model, this was the presence or absence of a highly correlated COVID-19 case count trajectory between two given counties in Missouri. Covariates included each county's total population, percent rurality, and distance between each county pair.

RESULTS

We found that all covariates were significantly associated with two counties having highly correlated COVID-19 case count trajectories. As the log of a county's total population increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 66% (odds ratio [OR] 1.66, 95% CI 1.43-1.92). As the percent of a county classified as rural increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 1% (OR 1.01, 95% CI 1.00-1.01). As the distance (in miles) between two counties increased, the odds of two counties having highly correlated COVID-19 case count trajectories decreased by 43% (OR 0.57, 95% CI 0.43-0.77). Lastly, as the log of the SCI between two Missouri counties increased, the odds of those two counties having highly correlated COVID-19 case count trajectories significantly increased by 17% (OR 1.17, 95% CI 1.09-1.26).

CONCLUSIONS

These results could suggest that two counties with a greater likelihood of sharing Facebook friendships means residents of those counties have a higher likelihood of sharing similar belief systems, in particular as they relate to COVID-19 and public health practices. Another possibility is that the SCI is picking up travel or movement data among county residents. This suggests the SCI is capturing a unique phenomenon relevant to COVID-19 and that it may be worth adding to other COVID-19 models. Additional research is needed to better understand what the SCI is capturing practically and what it means for public health policies and prevention practices.

Geospatial analysis and distribution patterns of home nursing care in a metropolitan area - a large-scale analysis

AIMS

This study focuses on home nursing care distribution in an urban setting in Germany.

BACKGROUND

A shortage of nursing care workforce is present in Germany.

METHODS

A geospatial analysis was performed to examine distribution patterns at the district level in Frankfurt, Germany (n = 46 districts) and factors were analysed influencing the location choice of home nursing care providers (n = 151). Furthermore, within the analysis we focused on the population aged over 65 years to model the demand for nursing care.

RESULTS

The analysis revealed a tendency of home nursing care providers to be located near the city centre (centripetal distribution pattern). However, the demand for care showed more inconsistent patterns. Still, a centripetal distribution pattern of demand could be stated. Compared with the control groups (e.g. acute hospitals and pharmacies) similar geographical distribution patterns were present. However, the location of home nursing care providers was less influenced by demand compared with the control groups.

CONCLUSION

The supply of nursing care was unevenly distributed in this metropolitan setting, but still matched the demand for nursing care.

IMPLICATION FOR NURSING MANAGEMENT

Due to the rapidly changing health care environments policy, regulations must be (re-)evaluated critically to improve the management and delivery of nursing care provision.