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Aim: To examine the diagnostic reasoning skills of two nurse practitioner student cohorts. Introduction: Nurse practitioners continue to play a pivotal role in health care provision. Diagnostic reasoning is a core skill of advanced practice. A comparative diagnostic reasoning study between two student cohorts was undertaken between 2018 and 2019. Methods: A validated diagnostic reasoning scale was completed by nurse practitioner students in the United Kingdom and the United States of America at the beginning and end of their clinical placements. The study utilized descriptive quantitative statistics from the data submitted by 22 survey respondents from an online survey. Results: Analysis of the diagnostic reasoning skills suggested there was a difference in the structural memory scores when compared with the beginning of their clinical placements and the final semester of their clinical placements. Conclusion: Diagnostic reasoning skills are a core skill of advanced practice. As students progress through their training, these skills improve. Despite the statistical difference in diagnostic reasoning scores, caution should be made in determining any larger implications due to the small number of participants in this study. Further study is needed in this area. Summary statement: What is already known about this topic? Diagnostic reasoning is a core skill for nurse practitioners.There is minimal research in this area.Diagnostic accuracy is vital for ensuring patient safety. What this paper adds? Structural memory scores improved at the end of nurse practitioner clinical placements. What are the implications of this paper for policy/practice/research/education? Further global research in the field of diagnostic reasoning for nurse practitioners and nurse practitioner students is needed to validate these findings.It is important to ensure nurse practitioners have competency in diagnostic reasoning to reduce diagnostic error.Curricula could be designed to incorporate the use of the DTI to evaluate a student's progress throughout the clinical portion of the nurse practitioner programme. [ABSTRACT FROM AUTHOR]

Aims To investigate possible factors related to patient monitoring to explain the higher mortality rates associated with after-hours transfers compared with daytime transfers from critical care units to the wards. Background International research suggests that patients transferred from critical care units after-hours have a higher mortality rate than transfers during daytime, although the reasons remain unknown. Design A prospective exploratory study. Methods Twenty-nine patients transferred from a UK critical care unit to a ward within the same hospital after-hours for 10 weeks beginning April 2009 were compared with 29 transfers during daytime hours matched on potentially confounding characteristics. UK Critical Care Unit transfer guidelines have remained unchanged since data collection. Outcomes were as follows: (i) frequency of nursing observations; (ii) time periods from transfer to first medical review; (iii) time period from transfer to first clinical observations; (iv) frequency of transfer to an inappropriate ward; (v) delayed transfers from Critical Care Unit to ward. Results Using Wilcoxon's Rank test (two tail) to compare paired data from the matched groups, observations were recorded significantly less frequently within the first 12 hours for after-hours transfers. Time from transfer to first clinical observations was significantly longer for after-hour transfer patients. The delay from when the patient was ready for ward care and actual transfer was also longer for the after-hours transfer group. Conclusions Surveillance differences, including time to the first set of observations and frequency of observations in the first 12 hours, are potential factors that may explain the differential mortality associated with after-hours transfers. [ABSTRACT FROM AUTHOR]

Political scientists often theorize that an explanatory variable should have 'no effect' and support this claim by demonstrating that its coefficient's estimate is not statistically significant. This empirical argument is quite weak, but I introduce applied researchers to simple, powerful tools that can strengthen their arguments for this hypothesis. With several supporting examples, I illustrate that researchers can use 90% confidence intervals to argue against meaningful effects and provide persuasive evidence for their hypothesis. [ABSTRACT FROM AUTHOR]

Aims This article reports on a study which examined the predictive relationship between emotional intelligence and the following programme outcomes for student nurses: clinical practice performance; academic performance and retention. Background In the context of concerns about a lack of compassionate care and amid calls for values-based selection procedures, emotional intelligence is emerging as a potential factor which might help the nursing profession to address some of these concerns. Design The study employed a longitudinal survey. Methods Student nurse applicants ( n = 307) were asked to complete self-report scales to establish a total score and four subscores for emotional intelligence and these scores were matched to individual student's performance on the undergraduate programme. The scales were completed between June-September 2007 and performance data were collected up until January 2009. Results A significant predictive relationship was found between emotional intelligence and all three programme outcomes: practice performance; academic performance and retention, after controlling for prior academic achievement, age and gender. There was a zero Pearson correlation between prior academic attainment and overall emotional intelligence, indicating that both predict different outcomes. Conclusions Recruitment and selection procedures should consider emotional intelligence as a legitimate additional entrance criterion for student nurses and further research is required to examine the potential relationship between emotional intelligence and compassionate care. [ABSTRACT FROM AUTHOR]

Aim. The aims of this study were to highlight the problems associated with missing data in healthcare research and to demonstrate the use of several techniques for dealing with missing values, through the use of an illustrative example. Background. In healthcare research studies, it is almost impossible to avoid at least some missing values during data collection, which in turn can threaten the validity of the study conclusions. A range of methods for reducing the impact of missing data on the validity of study findings have been developed, depending on the nature and patterns which the missing values may take. Design. A discursive study. Methods. Several techniques designed to deal with missing data are described and applied to an illustrative example. These methods include complete-case analysis, available-case analysis, as well as single and multiple imputation. Conclusions. If research data contain missing values that are not randomly distributed, then the study results are likely to be biased unless an effective approach to dealing with the missing values is implemented. Relevance to clinical practice. If nursing and healthcare practice is to be informed by research findings, then these findings must be reliable and valid. Researchers should report the details of missing data, and appropriate methods for dealing with missing values should be incorporated into the data analysis. [ABSTRACT FROM AUTHOR]