Aim
This study examined the prevalence of job dissatisfaction and burnout among maternity nurses and the association of job dissatisfaction and burnout with missed care.
Background
Nurse burnout and ...job dissatisfaction affect the quality and safety of care and are amenable to intervention. Little is known about job dissatisfaction and burnout among maternity nurses or how these factors are associated with missed care in maternity units.
Methods
This was a cross‐sectional secondary analysis of the 2015 RN4CAST survey data and the American Hospital Association's 2015 Annual Survey. Robust logistic regression models at the nurse level examined the association of job dissatisfaction and burnout with missed care.
Results
One‐quarter of nurses screened positive for burnout, and almost one‐fifth reported job dissatisfaction. While 56.4% of nurses in the total sample reported any missed care, 72.6% of nurses with job dissatisfaction and 84.5% of nurses with burnout reported any missed care (p < .001).
Conclusions
The association of job dissatisfaction and burnout, which are modifiable states, with increased rates of missed maternity care suggests that addressing job dissatisfaction and burnout may improve care quality.
Implications for Nursing Management
Job dissatisfaction, burnout and missed care may decrease with an improved work environment.
AimsTo explore factors associated with nurses' moral distress during the first COVID‐19 surge and their longer‐term mental health.DesignCross‐sectional, correlational survey study.MethodsRegistered ...nurses were surveyed in September 2020 about their experiences during the first peak month of COVID‐19 using the new, validated, COVID‐19 Moral Distress Scale for Nurses. Nurses' mental health was measured by recently experienced symptoms. Analyses included descriptive statistics and regression analysis. Outcome variables were moral distress and mental health. Explanatory variables were frequency of COVID‐19 patients, leadership communication and personal protective equipment/cleaning supplies access. The sample comprised 307 nurses (43% response rate) from two academic medical centres. ResultsMany respondents had difficulty accessing personal protective equipment. Most nurses reported that hospital leadership communication was transparent, effective and timely. The most distressing situations were the transmission risk to nurses' family members, caring for patients without family members present, and caring for patients dying without family or clergy present. These occurred occasionally with moderate distress. Nurses reported 2.5 days each in the past week of feeling anxiety, withdrawn and having difficulty sleeping. Moral distress decreased with effective communication and access to personal protective equipment. Moral distress was associated with longer‐term mental health.ConclusionPandemic patient care situations are the greatest sources of nurses' moral distress. Effective leadership communication, fewer COVID‐19 patients, and access to protective equipment decrease moral distress, which influences longer‐term mental health.ImpactLittle was known about the impact of COVID‐19 on nurses' moral distress. We found that nurses' moral distress was associated with the volume of care for infected patients, access to personal protective equipment, and communication from leaders. We found that moral distress was associated with longer‐term mental health. Leaders should communicate transparently to decrease nurses' moral distress and the negative effects of global crises on nurses' longer‐term mental health.
Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve ...statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables' contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. To provide an example of the different algorithms' function, they were used on a completed cross‐sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods.
Variations in nursing practice were observed across our hospital, a 520-bed acute care teaching facility in the northeast United States, regarding the timing and frequency of insulin administration ...in adult patients with diabetes. Chart audits noted that RNs administered insulin more than one hour after blood glucose results were obtained 97% of the time. In addition, insulin was given at bedtime only 37% of the time.
The purpose of this quality improvement (QI) project was to improve the care of inpatients requiring insulin by implementing protocols and adjusting practice to align with best practice recommendations.
The clinical nurse education specialist met with a team of staff nurses, providers, nurse leaders, and patient care technicians (PCTs) to formulate protocols and design interventions to ensure improvements in the quality of care for inpatients with diabetes. A sequence of education sessions and an online learning module were developed and assigned to nurses and PCTs to address knowledge gaps, specifically in the pharmacodynamics and safe administration of insulin, as well as how best to provide care to patients with diabetes. Monthly adherence data were disseminated to nurse leaders and educators and reviewed with clinical staff at daily safety huddles and staff meetings. Additional interventions to enhance nursing practice in caring for patients with diabetes included ensuring both bedtime insulin administration and timely insulin delivery. This project began in May 2017 and ended five years later.
Two weeks after initial education sessions began in May and June 2017, the frequency of giving bedtime insulin based on the order set and according to the patient's blood glucose levels rose from 37% to 82%, and adherence to best practice protocols continued until final chart audits were performed in May 2022. The frequency of giving insulin less than one hour after obtaining blood glucose results improved from 3% to 64% between October and December 2019, and increased to a sustained level above the project's 92% goal two years later.
Protocol development, targeted education, and audits with feedback led to improved care delivery for patients requiring insulin and increased nursing confidence.
Background
Birth is the most common reason for hospitalization in the United States. Hospital variation in maternal outcomes is an important indicator of health care quality. Spontaneous vaginal ...birth (SVB) is the most optimal birth outcome for the majority of mothers and newborns. The purpose of this study was to examine hospital‐level variation in SVB overall and among low‐risk women in a four‐state sample representing 25% of births in the United States in 2016.
Methods
Women giving birth in California, Pennsylvania, New Jersey, and Florida were identified in 2016 state discharge s. Patient data were merged with hospital data from the American Hospital Association's (AHA) 2016 Annual Survey. Overall and low‐risk SVB rates were calculated for each hospital in the sample and stratified by bed size, teaching status, rurality, birth volume, and state.
Results
Our final sample included 869 681 women who gave birth in 494 hospitals. The mean overall SVB rate in the sample was 61.1%, ranging from 16.8% to 79.9%. The mean low‐risk SVB rate was 78% and ranged from 34.6% to 93.3%. Variation in SVB rates cut across all the hospital structural characteristic strata.
Discussion
The wide variation in SVB rates indicates significant room for improvement in this maternal quality metric. Our finding, that hospitals of all types and locations had both low and high SVB rates, suggests that excellent maternal outcomes are possible in all hospital settings. The variation in SVB rates across hospitals warrants research into modifiable hospital factors that may be influencing SVB rates.
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