Sleep Quality and Daytime Sleepiness in Prelicensure Baccalaureate Nursing Students.

BACKGROUND: Nursing students need to learn about sleep health to provide safe patient care. The purpose of this study was to investigate sleep in nursing students and describe factors that affect their sleep. METHOD: This study used a cross-sectional descriptive design with a convenience sample from baccalaureate nursing programs in a midwestern region of the United States. Data were collected using a demographic questionnaire, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and Sleep Hygiene Index. RESULTS: Two hundred fifty-four nursing students reported poor sleep quality, excessive daytime sleepiness, and maladaptive sleep hygiene, regardless of their year of study or enrollment status. Behavior of technology use into the night was the most frequent reason why students lost sleep. CONCLUSION: Learning the importance of sleep hygiene, good sleep quality, and the associated health benefits may assist nursing students with achieving optimal daytime functioning. Consideration should be given to sleep health content as a thread through nursing curriculum. [J Nurs Educ. 2021;60(4):196-202.].

Person-Fit as an Index of Inattentive Responding: A Comparison of Methods Using Polytomous Survey Data.

Self-report measures are vulnerable to response biases that can degrade the accuracy of conclusions drawn from results. In low-stakes measures, inattentive or careless responding can be especially problematic. A variety of a priori and post hoc methods exist for detecting these aberrant response patterns. Previous research indicates that nonparametric person-fit statistics tend to be the most accurate post hoc method for detecting inattentive responding on measures with dichotomous outcomes. This study investigated the accuracy and impact on model fit of parametric and nonparametric person-fit statistics in detecting inattentive responding with polytomous response scales. Receiver operating curve (ROC) analysis was used to determine the accuracy of each detection metric, and confirmatory factor analysis (CFA) fit indices were used to examine the impact of using person-fit statistics to identify inattentive respondents. ROC analysis showed the nonparametric H T statistic offered the most area under the curve when predicting a proxy for inattentive responding. The CFA fit indices showed the impact of using the person-fit statistics largely depends on the purpose (and cutoff) for using the person-fit statistics. Implications for using person-fit statistics to identify inattentive responders are discussed further.