Secondary Qualitative Analysis of Moral Injury in Obstetric and Neonatal Nurses

ObjectiveTo investigate the presence of symptoms of moral injury in obstetric and neonatal nurses.DesignA secondary qualitative analysis using an analytic expansion of three primary studies.SettingPostal mail and electronic surveys.ParticipantsI used three primary studies: participants in the first consisted of 78 labor and delivery nurses, participants in the second consisted of 75 nurse-midwives, and participants in the third consisted of 22 NICU nurses.MethodsI used Krippendorff’s content analysis method for qualitative data to reanalyze the three primary data sets. The categories I used in this analysis were the 10 symptoms of moral injury that are assessed by the Moral Injury Symptoms Scale–Health Professionals Version.ResultsWhen combining the three types of obstetric and neonatal participants, the top three most frequently cited symptoms of moral injury were moral concern, guilt, and self-condemnation. For participants in labor and delivery units and NICUs, moral concern was the most often described symptom, whereas for participants in midwifery it was guilt. None of the participants reported loss of meaning in their lives, loss of faith, or religious struggle. Participants who worked in NICUs did not describe any symptoms of shame or difficulty forgiving.ConclusionIn addition to the primary symptoms of moral injury, reported secondary consequences of moral injury can include depression, anxiety, anger, self-harm, and social problems. Interventions such as acceptance and commitment therapy are needed to help nurses address the potential for moral injury and repair its effects. Since the COVID-19 pandemic, now more than ever, moral injury needs to be recognized in obstetric and neonatal nurses and not just in the military population.     

Moral Distress and Burnout in NICU Nurses

The objectives of this study were to factor analyze the Moral Distress Scale–Revised (MDS-R) in NICU nurses and to evaluate the relationships among dimensions of the MDS-R and the demoralization, exhaustion, and loss of motive dimensions of the Burnout Measure (BM). A total of 142 NICU nurses completed modified pen-and-paper versions of the MDS-R and BM. Exploratory and confirmatory factor analyses showed that the MDS-R-14 was a relatively good fit for the data. The compromised care dimension predicted BM demoralization (β = 0.24) and exhaustion (β = 0.22), the futile care dimension predicted BM exhaustion (β = 0.18), and the untruthful care dimension predicted BM demoralization (β = .25). Strategies to mitigate moral distress and resulting burnout in NICU nurses should address futile care, compromised care, and untruthful care.     

Systematic review: Predictors of students’ success in baccalaureate nursing programs

Nursing schools strive to select a diverse student population who are likely to succeed by ensuring timely student progression through the program and effective use of educational sources. The purpose of this systematic literature review is to explore the preadmission variables and selection criteria that predict student success in 4-year baccalaureate nursing programs in the U.S. Sixteen articles met the eligibility criteria, and six measures were used to define student success: (a) early academic success, particularly during the first and second year; (b) attrition; (c) timely completion of the program; (d) graduation; (e) performance in nursing courses; and (f) academic performance in other science courses. Typically, the core set of cognitive predictors used in the admission process in nursing schools were pre-nursing GPA, pre-nursing collegiate science GPA, and scores on standardized aptitude exams. This review suggests that it is challenging to isolate one single variable as the best predictor of student success; however, using a combination of variables can offer a reliable prediction method. More researchers should consider using a theoretical basis to guide their inquiry on this topic. Additionally, researchers should examine admission variables that are most relevant across programs.